From 7d6e7f97b654ceaa1939a5dc2a150ba4ff733d69 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Morais?= <19170303+jmoraispk@users.noreply.github.com> Date: Sat, 28 Jun 2025 14:33:02 -0400 Subject: [PATCH 1/7] update deepmimo tutorial to v4 --- tutorials/phy/DeepMIMO.ipynb | 1197 ++++++++++++++++++---------------- 1 file changed, 619 insertions(+), 578 deletions(-) diff --git a/tutorials/phy/DeepMIMO.ipynb b/tutorials/phy/DeepMIMO.ipynb index cffb0c03c..ee8fc242e 100644 --- a/tutorials/phy/DeepMIMO.ipynb +++ b/tutorials/phy/DeepMIMO.ipynb @@ -1,594 +1,635 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using the DeepMIMO Dataset with Sionna\n", - "\n", - "In this example, you will learn how to use the ray-tracing based DeepMIMO dataset." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[DeepMIMO](https://deepmimo.net/) is a generic dataset that enables a wide range of machine/deep learning applications for MIMO systems. It takes as input a set of parameters (such as antenna array configurations and time-domain/OFDM parameters) and generates MIMO channel realizations, corresponding locations, angles of arrival/departure, etc., based on these parameters and on a ray-tracing scenario selected [from those available in DeepMIMO](https://deepmimo.net/scenarios/).\n", - "\n", - "## Table of Contents\n", - "* [GPU Configuration and Imports](#GPU-Configuration-and-Imports)\n", - "* [Configuration of DeepMIMO](#Configuration-of-DeepMIMO)\n", - "* [Using DeepMIMO with Sionna](#Using-DeepMIMO-with-Sionna)\n", - "* [Link-level Simulations using Sionna and DeepMIMO](#Link-level-Simulations-using-Sionna-and-DeepMIMO)\n", - "* [DeepMIMO License and Citation](#DeepMIMO-License-and-Citation)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## GPU Configuration and Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:42.830858Z", - "iopub.status.busy": "2025-03-13T01:48:42.830249Z", - "iopub.status.idle": "2025-03-13T01:48:45.899584Z", - "shell.execute_reply": "2025-03-13T01:48:45.898771Z" - } - }, - "outputs": [], - "source": [ - "import os\n", - "if os.getenv(\"CUDA_VISIBLE_DEVICES\") is None:\n", - " gpu_num = 0 # Use \"\" to use the CPU\n", - " os.environ[\"CUDA_VISIBLE_DEVICES\"] = f\"{gpu_num}\"\n", - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n", - "\n", - "# Import Sionna\n", - "try:\n", - " import sionna.phy\n", - "except ImportError as e:\n", - " import sys\n", - " if 'google.colab' in sys.modules:\n", - " # Install Sionna in Google Colab\n", - " print(\"Installing Sionna and restarting the runtime. Please run the cell again.\")\n", - " os.system(\"pip install sionna\")\n", - " os.kill(os.getpid(), 5)\n", - " else:\n", - " raise e\n", - "\n", - "# Configure the notebook to use only a single GPU and allocate only as much memory as needed\n", - "# For more details, see https://www.tensorflow.org/guide/gpu\n", - "import tensorflow as tf\n", - "gpus = tf.config.list_physical_devices('GPU')\n", - "if gpus:\n", - " try:\n", - " tf.config.experimental.set_memory_growth(gpus[0], True)\n", - " except RuntimeError as e:\n", - " print(e)\n", - "# Avoid warnings from TensorFlow\n", - "tf.get_logger().setLevel('ERROR')\n", - "\n", - "sionna.phy.config.seed = 42 # Set seed for reproducible random number generation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:45.903639Z", - "iopub.status.busy": "2025-03-13T01:48:45.903247Z", - "iopub.status.idle": "2025-03-13T01:48:45.914833Z", - "shell.execute_reply": "2025-03-13T01:48:45.913951Z" - } - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Load the required Sionna components\n", - "from sionna.phy import Block\n", - "from sionna.phy.mimo import StreamManagement\n", - "from sionna.phy.ofdm import ResourceGrid, ResourceGridMapper, LSChannelEstimator, \\\n", - " LMMSEEqualizer, RZFPrecoder, RemoveNulledSubcarriers\n", - "from sionna.phy.channel import subcarrier_frequencies, ApplyOFDMChannel, \\\n", - " GenerateOFDMChannel, CIRDataset\n", - "from sionna.phy.fec.ldpc import LDPC5GEncoder, LDPC5GDecoder\n", - "from sionna.phy.mapping import BinarySource, Mapper, Demapper\n", - "from sionna.phy.utils import ebnodb2no, sim_ber\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configuration of DeepMIMO" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "DeepMIMO provides multiple [scenarios](https://deepmimo.net/scenarios/) that one can select from. In this example, we use the O1 scenario with the carrier frequency set to 60 GHz (O1_60). To run this example, please download the \"O1_60\" data files [from this page](https://deepmimo.net/scenarios/o1-scenario/). The downloaded zip file should be extracted into a folder, and the parameter `DeepMIMO_params['dataset_folder']` should be set to point to this folder, as done below.\n", - "\n", - "To use DeepMIMO with Sionna, the DeepMIMO dataset first needs to be generated. The generated DeepMIMO dataset contains channels for different locations of the users and basestations. The layout of the O1 scenario is shown in the figure below. \n", - "\n", - 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- "\n", - "In this example, we generate a dataset that consists of channels for the links from the basestation 6 to the users located on the rows 400 to 450. Each of these rows consists of 181 user locations, resulting in $51 \\times 181 = 9231$ basestation-user channels.\n", - "\n", - "The antenna arrays in the DeepMIMO dataset are defined through the x-y-z axes. In the following example, a single-user MISO downlink is considered. The basestation is equipped with a uniform linear array of 16 elements spread along the x-axis. The users are each equipped with a single antenna. These parameters can be configured using the code below (for more information about the DeepMIMO parameters, please check [the DeepMIMO configurations](https://deepmimo.net/versions/v2-python/))." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:45.918949Z", - "iopub.status.busy": "2025-03-13T01:48:45.918713Z", - "iopub.status.idle": "2025-03-13T01:48:52.133244Z", - "shell.execute_reply": "2025-03-13T01:48:52.132383Z" - } - }, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting DeepMIMO\n", - " Downloading DeepMIMO-1.0-py3-none-any.whl.metadata (421 bytes)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.10/site-packages (from DeepMIMO) (1.26.4)\n", - "Requirement already satisfied: scipy in /usr/local/lib/python3.10/site-packages (from DeepMIMO) (1.15.2)\n", - "Collecting tqdm (from DeepMIMO)\n", - " Downloading tqdm-4.67.1-py3-none-any.whl.metadata (57 kB)\n", - "Downloading DeepMIMO-1.0-py3-none-any.whl (21 kB)\n", - "Downloading tqdm-4.67.1-py3-none-any.whl (78 kB)\n", - "Installing collected packages: tqdm, DeepMIMO\n", - "Successfully installed DeepMIMO-1.0 tqdm-4.67.1\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "lSbcn0aLtPmH" + }, + "source": [ + "# Using the DeepMIMO Dataset with Sionna\n", + "\n", + "In this example, you will learn how to use the ray-tracing based DeepMIMO dataset." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Basestation 6\n", - "\n", - "UE-BS Channels\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "Thv9PInhtPmI" + }, + "source": [ + "[DeepMIMO](https://deepmimo.net/) is a generic dataset that enables a wide range of machine/deep learning applications for MIMO systems. It takes as input a set of parameters (such as antenna array configurations and time-domain/OFDM parameters) and generates MIMO channel realizations, corresponding locations, angles of arrival/departure, etc., based on these parameters and on a ray-tracing scenario selected [from those available in DeepMIMO](https://deepmimo.net/scenarios/).\n", + "\n", + "## Table of Contents\n", + "* [GPU Configuration and Imports](#GPU-Configuration-and-Imports)\n", + "* [Configuration of DeepMIMO](#Configuration-of-DeepMIMO)\n", + "* [Using DeepMIMO with Sionna](#Using-DeepMIMO-with-Sionna)\n", + "* [Link-level Simulations using Sionna and DeepMIMO](#Link-level-Simulations-using-Sionna-and-DeepMIMO)\n", + "* [DeepMIMO License and Citation](#DeepMIMO-License-and-Citation)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "BS-BS Channels\n" - ] - } - ], - "source": [ - "# Import DeepMIMO\n", - "try:\n", - " import DeepMIMO\n", - "except ImportError as e:\n", - " # Install DeepMIMO if package is not already installed\n", - " import os\n", - " os.system(\"pip install DeepMIMO\")\n", - " import DeepMIMO\n", - "\n", - "# Channel generation\n", - "DeepMIMO_params = DeepMIMO.default_params() # Load the default parameters\n", - "DeepMIMO_params['dataset_folder'] = r'./scenarios' # Path to the downloaded scenarios\n", - "DeepMIMO_params['scenario'] = 'O1_60' # DeepMIMO scenario\n", - "DeepMIMO_params['num_paths'] = 10 # Maximum number of paths\n", - "DeepMIMO_params['active_BS'] = np.array([6]) # Basestation indices to be included in the dataset\n", - "\n", - "# Selected rows of users, whose channels are to be generated.\n", - "DeepMIMO_params['user_row_first'] = 400 # First user row to be included in the dataset\n", - "DeepMIMO_params['user_row_last'] = 450 # Last user row to be included in the dataset\n", - "\n", - "# Configuration of the antenna arrays\n", - "DeepMIMO_params['bs_antenna']['shape'] = np.array([16, 1, 1]) # BS antenna shape through [x, y, z] axes\n", - "DeepMIMO_params['ue_antenna']['shape'] = np.array([1, 1, 1]) # UE antenna shape through [x, y, z] axes\n", - "\n", - "# The OFDM_channels parameter allows choosing between the generation of channel impulse\n", - "# responses (if set to 0) or frequency domain channels (if set to 1).\n", - "# It is set to 0 for this simulation, as the channel responses in frequency domain\n", - "# will be generated using Sionna.\n", - "DeepMIMO_params['OFDM_channels'] = 0\n", - "\n", - "# Generates a DeepMIMO dataset\n", - "DeepMIMO_dataset = DeepMIMO.generate_data(DeepMIMO_params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualization of the dataset\n", - "\n", - "To provide a better understanding of the user and basestation locations, we next visualize the locations of the users, highlighting the first active row of users (row 400), and basestation 6." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:52.136681Z", - "iopub.status.busy": "2025-03-13T01:48:52.136424Z", - "iopub.status.idle": "2025-03-13T01:48:52.372533Z", - "shell.execute_reply": "2025-03-13T01:48:52.371889Z" - } - }, - "outputs": [ + "cell_type": "markdown", + "metadata": { + "id": "G-qy_3RatPmI" + }, + "source": [ + "## GPU Configuration and Imports" + ] + }, { - "data": { - "image/png": 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", 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" + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:42.830858Z", + "iopub.status.busy": "2025-03-13T01:48:42.830249Z", + "iopub.status.idle": "2025-03-13T01:48:45.899584Z", + "shell.execute_reply": "2025-03-13T01:48:45.898771Z" + }, + "id": "yuejsob6tPmJ" + }, + "outputs": [], + "source": [ + "import os\n", + "if os.getenv(\"CUDA_VISIBLE_DEVICES\") is None:\n", + " gpu_num = 0 # Use \"\" to use the CPU\n", + " os.environ[\"CUDA_VISIBLE_DEVICES\"] = f\"{gpu_num}\"\n", + "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n", + "\n", + "# Import Sionna\n", + "try:\n", + " import sionna.phy\n", + "except ImportError as e:\n", + " import sys\n", + " if 'google.colab' in sys.modules:\n", + " # Install Sionna in Google Colab\n", + " print(\"Installing Sionna and restarting the runtime. Please run the cell again.\")\n", + " os.system(\"pip install sionna\")\n", + " os.kill(os.getpid(), 5)\n", + " else:\n", + " raise e\n", + "\n", + "# Configure the notebook to use only a single GPU and allocate only as much memory as needed\n", + "# For more details, see https://www.tensorflow.org/guide/gpu\n", + "import tensorflow as tf\n", + "gpus = tf.config.list_physical_devices('GPU')\n", + "if gpus:\n", + " try:\n", + " tf.config.experimental.set_memory_growth(gpus[0], True)\n", + " except RuntimeError as e:\n", + " print(e)\n", + "# Avoid warnings from TensorFlow\n", + "tf.get_logger().setLevel('ERROR')\n", + "\n", + "sionna.phy.config.seed = 42 # Set seed for reproducible random number generation" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12,8))\n", - "\n", - "## User locations\n", - "active_bs_idx = 0 # Select the first active basestation in the dataset\n", - "plt.scatter(DeepMIMO_dataset[active_bs_idx]['user']['location'][:, 1], # y-axis location of the users\n", - " DeepMIMO_dataset[active_bs_idx]['user']['location'][:, 0], # x-axis location of the users\n", - " s=1, marker='x', c='C0', label='The users located on the rows %i to %i (R%i to R%i)'%\n", - " (DeepMIMO_params['user_row_first'], DeepMIMO_params['user_row_last'],\n", - " DeepMIMO_params['user_row_first'], DeepMIMO_params['user_row_last']))\n", - "# First 181 users correspond to the first row\n", - "plt.scatter(DeepMIMO_dataset[active_bs_idx]['user']['location'][0:181, 1],\n", - " DeepMIMO_dataset[active_bs_idx]['user']['location'][0:181, 0],\n", - " s=1, marker='x', c='C1', label='First row of users (R%i)'% (DeepMIMO_params['user_row_first']))\n", - "\n", - "## Basestation location\n", - "plt.scatter(DeepMIMO_dataset[active_bs_idx]['location'][1],\n", - " DeepMIMO_dataset[active_bs_idx]['location'][0],\n", - " s=50.0, marker='o', c='C2', label='Basestation')\n", - "\n", - "plt.gca().invert_xaxis() # Invert the x-axis to align the figure with the figure above\n", - "plt.ylabel('x-axis')\n", - "plt.xlabel('y-axis')\n", - "plt.grid()\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using DeepMIMO with Sionna\n", - "\n", - "The DeepMIMO Python package provides [a Sionna-compliant channel impulse response generator](https://nvlabs.github.io/sionna/phy/tutorials/CIR_Dataset.html#Generators) that adapts the structure of the DeepMIMO dataset to be consistent with Sionna.\n", - "\n", - "An adapter is instantiated for a given DeepMIMO dataset. In addition to the dataset, the adapter takes the indices of the basestations and users, to generate the channels between these basestations and users:\n", - "\n", - "`DeepMIMOSionnaAdapter(DeepMIMO_dataset, bs_idx, ue_idx)`\n", - "\n", - "\n", - "**Note:** `bs_idx` and `ue_idx` set the links from which the channels are drawn. For instance, if `bs_idx = [0, 1]` and `ue_idx = [2, 3]`, the adapter then outputs the 4 channels formed by the combination of the first and second basestations with the third and fourth users.\n", - "\n", - "The default behavior for `bs_idx` and `ue_idx` are defined as follows:\n", - "- If value for `bs_idx` is not given, it will be set to `[0]` (i.e., the first basestation in the `DeepMIMO_dataset`).\n", - "- If value for `ue_idx` is not given, then channels are provided for the links between the `bs_idx` and all users (i.e., `ue_idx=range(len(DeepMIMO_dataset[0]['user']['channel']))`.\n", - "- If the both `bs_idx` and `ue_idx` are not given, the channels between the first basestation and all the users are provided by the adapter. For this example, `DeepMIMOSionnaAdapter(DeepMIMO_dataset)` returns the channels from the basestation 6 and the 9231 available user locations.\n", - "\n", - "**Note:** The adapter assumes basestations are transmitters and users are receivers. Uplink channels can be obtained using (transpose) reciprocity.\n", - "\n", - "### Random Sampling of Multi-User Channels\n", - "\n", - "When considering multiple basestations, `bs_idx` can be set to a 2D numpy matrix of shape $($ # of samples $\\times$ # of basestations per sample $)$. In this case, for each sample of basestations, the `DeepMIMOSionnaAdapter` returns a set of $($ # of basestations per sample $\\times$ # of users $)$ channels, which can be provided as a multi-transmitter sample for the Sionna model. For example, `bs_idx = np.array([[0, 1], [2, 3], [4, 5]])` provides three sets of $($ 2 basestations $\\times$ # of users $)$ channels. These three channel sets are from the basestation sets `[0, 1]`, `[2, 3]`, and `[4, 5]`, respectively, to the users.\n", - "\n", - "To use the adapter for multi-user channels, `ue_idx` can be set to a 2D numpy matrix of shape $($ # of samples $\\times$ # of users per sample $)$. In this case, for each sample of users, the `DeepMIMOSionnaAdapter` returns a set of $($ # of basestations $\\times$ # of users per sample $)$ channels, which can be provided as a multi-receiver sample for the Sionna model. For example, `ue_idx = np.array([[0, 1 ,2], [4, 5, 6]])` provides two sets of $($ # of basestations $\\times$ 3 users $)$ channels. These two channel sets are from the basestations to the user sets `[0, 1, 2]` and `[4, 5, 6]`, respectively.\n", - "\n", - "In order to randomly sample channels from all the available user locations considering `num_rx` users, one may set `ue_idx` as in the following cell. In this example, the channels will be randomly chosen from the links between the basestation 6 and the 9231 available user locations." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:52.376377Z", - "iopub.status.busy": "2025-03-13T01:48:52.376179Z", - "iopub.status.idle": "2025-03-13T01:48:52.380757Z", - "shell.execute_reply": "2025-03-13T01:48:52.380192Z" - } - }, - "outputs": [], - "source": [ - "from DeepMIMO import DeepMIMOSionnaAdapter\n", - "\n", - "# Number of receivers for the Sionna model.\n", - "# MISO is considered here.\n", - "num_rx = 1\n", - "\n", - "# The number of UE locations in the generated DeepMIMO dataset\n", - "num_ue_locations = len(DeepMIMO_dataset[0]['user']['channel']) # 9231\n", - "# Pick the largest possible number of user locations that is a multiple of ``num_rx``\n", - "ue_idx = np.arange(num_rx*(num_ue_locations//num_rx))\n", - "# Optionally shuffle the dataset to not select only users that are near each others\n", - "np.random.shuffle(ue_idx)\n", - "# Reshape to fit the requested number of users\n", - "ue_idx = np.reshape(ue_idx, [-1, num_rx]) # In the shape of (floor(9231/num_rx) x num_rx)\n", - "\n", - "DeepMIMO_Sionna_adapter = DeepMIMOSionnaAdapter(DeepMIMO_dataset, ue_idx=ue_idx)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Link-level Simulations using Sionna and DeepMIMO\n", - "\n", - "In the following cell, we define a Sionna model implementing the end-to-end link.\n", - "\n", - "**Note:** The Sionna CIRDataset object shuffles the DeepMIMO channels provided by the adapter. Therefore, channel samples are passed through the model in a random order." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:52.384454Z", - "iopub.status.busy": "2025-03-13T01:48:52.384238Z", - "iopub.status.idle": "2025-03-13T01:48:52.397002Z", - "shell.execute_reply": "2025-03-13T01:48:52.396376Z" - } - }, - "outputs": [], - "source": [ - "class LinkModel(Block):\n", - " def __init__(self,\n", - " DeepMIMO_Sionna_adapter,\n", - " carrier_frequency,\n", - " cyclic_prefix_length,\n", - " pilot_ofdm_symbol_indices,\n", - " subcarrier_spacing = 60e3,\n", - " batch_size = 64\n", - " ):\n", - " super().__init__()\n", - "\n", - " self._batch_size = batch_size\n", - " self._cyclic_prefix_length = cyclic_prefix_length\n", - " self._pilot_ofdm_symbol_indices = pilot_ofdm_symbol_indices\n", - "\n", - " # CIRDataset to parse the dataset\n", - " self._CIR = CIRDataset(DeepMIMO_Sionna_adapter,\n", - " self._batch_size,\n", - " DeepMIMO_Sionna_adapter.num_rx,\n", - " DeepMIMO_Sionna_adapter.num_rx_ant,\n", - " DeepMIMO_Sionna_adapter.num_tx,\n", - " DeepMIMO_Sionna_adapter.num_tx_ant,\n", - " DeepMIMO_Sionna_adapter.num_paths,\n", - " DeepMIMO_Sionna_adapter.num_time_steps)\n", - "\n", - " # System parameters\n", - " self._carrier_frequency = carrier_frequency\n", - " self._subcarrier_spacing = subcarrier_spacing\n", - " self._fft_size = 76\n", - " self._num_ofdm_symbols = 14\n", - " self._num_streams_per_tx = DeepMIMO_Sionna_adapter.num_rx\n", - " self._dc_null = False\n", - " self._num_guard_carriers = [0, 0]\n", - " self._pilot_pattern = \"kronecker\"\n", - " self._pilot_ofdm_symbol_indices = pilot_ofdm_symbol_indices\n", - " self._num_bits_per_symbol = 4\n", - " self._coderate = 0.5\n", - "\n", - " # Setup the OFDM resource grid and stream management\n", - " self._sm = StreamManagement(np.ones([DeepMIMO_Sionna_adapter.num_rx, 1], int), self._num_streams_per_tx)\n", - " self._rg = ResourceGrid(num_ofdm_symbols=self._num_ofdm_symbols,\n", - " fft_size=self._fft_size,\n", - " subcarrier_spacing = self._subcarrier_spacing,\n", - " num_tx=DeepMIMO_Sionna_adapter.num_tx,\n", - " num_streams_per_tx=self._num_streams_per_tx,\n", - " cyclic_prefix_length=self._cyclic_prefix_length,\n", - " num_guard_carriers=self._num_guard_carriers,\n", - " dc_null=self._dc_null,\n", - " pilot_pattern=self._pilot_pattern,\n", - " pilot_ofdm_symbol_indices=self._pilot_ofdm_symbol_indices)\n", - "\n", - " # Components forming the link\n", - "\n", - " # Codeword length\n", - " self._n = int(self._rg.num_data_symbols * self._num_bits_per_symbol)\n", - " # Number of information bits per codeword\n", - " self._k = int(self._n * self._coderate)\n", - "\n", - " # OFDM channel\n", - " self._frequencies = subcarrier_frequencies(self._rg.fft_size, self._rg.subcarrier_spacing)\n", - " self._ofdm_channel = GenerateOFDMChannel(self._CIR, self._rg, normalize_channel=True)\n", - " self._channel_freq = ApplyOFDMChannel(add_awgn=True)\n", - "\n", - " # Transmitter\n", - " self._binary_source = BinarySource()\n", - " self._encoder = LDPC5GEncoder(self._k, self._n)\n", - " self._mapper = Mapper(\"qam\", self._num_bits_per_symbol)\n", - " self._rg_mapper = ResourceGridMapper(self._rg)\n", - " self._zf_precoder = RZFPrecoder(self._rg, self._sm, return_effective_channel=True)\n", - "\n", - " # Receiver\n", - " self._ls_est = LSChannelEstimator(self._rg, interpolation_type=\"lin_time_avg\")\n", - " self._lmmse_equ = LMMSEEqualizer(self._rg, self._sm)\n", - " self._demapper = Demapper(\"app\", \"qam\", self._num_bits_per_symbol)\n", - " self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n", - " self._remove_nulled_scs = RemoveNulledSubcarriers(self._rg)\n", - "\n", - " def call(self, batch_size, ebno_db):\n", - "\n", - " # Transmitter\n", - " b = self._binary_source([self._batch_size, 1, self._num_streams_per_tx, self._k])\n", - " c = self._encoder(b)\n", - " x = self._mapper(c)\n", - " x_rg = self._rg_mapper(x)\n", - " # Generate the OFDM channel\n", - " h_freq = self._ofdm_channel()\n", - " # Precoding\n", - " x_rg, g = self._zf_precoder(x_rg, h_freq)\n", - "\n", - " # Apply OFDM channel\n", - " no = ebnodb2no(ebno_db, self._num_bits_per_symbol, self._coderate, self._rg)\n", - " y = self._channel_freq(x_rg, h_freq, no)\n", - "\n", - " # Receiver\n", - " h_hat, err_var = self._ls_est (y, no)\n", - " x_hat, no_eff = self._lmmse_equ(y, h_hat, err_var, no)\n", - " llr = self._demapper(x_hat, no_eff)\n", - " b_hat = self._decoder(llr)\n", - "\n", - " return b, b_hat" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We next evaluate the setup with different $E_b/N_0$ values to obtain BLER curves." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:52.400127Z", - "iopub.status.busy": "2025-03-13T01:48:52.399905Z", - "iopub.status.idle": "2025-03-13T01:49:23.855650Z", - "shell.execute_reply": "2025-03-13T01:49:23.854765Z" - } - }, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", - "---------------------------------------------------------------------------------------------------------------------------------------\n", - " -7.0 | 1.2337e-01 | 1.0000e+00 | 28803 | 233472 | 128 | 128 | 7.4 |reached target block errors\n", - " -6.806 | 9.1728e-02 | 9.9219e-01 | 21416 | 233472 | 127 | 128 | 0.1 |reached target block errors\n", - " -6.611 | 6.0393e-02 | 9.3750e-01 | 14100 | 233472 | 120 | 128 | 0.1 |reached target block errors\n", - " -6.417 | 2.7489e-02 | 7.6562e-01 | 9627 | 350208 | 147 | 192 | 0.1 |reached target block errors\n", - " -6.222 | 6.6111e-03 | 3.9844e-01 | 3087 | 466944 | 102 | 256 | 0.2 |reached target block errors\n", - " -6.028 | 9.2416e-04 | 9.9265e-02 | 1834 | 1984512 | 108 | 1088 | 0.9 |reached target block errors\n", - " -5.833 | 7.8896e-05 | 1.3594e-02 | 921 | 11673600 | 87 | 6400 | 5.1 |reached max iterations\n", - " -5.639 | 9.8513e-06 | 2.0313e-03 | 115 | 11673600 | 13 | 6400 | 5.2 |reached max iterations\n", - " -5.444 | 4.2832e-07 | 1.5625e-04 | 5 | 11673600 | 1 | 6400 | 5.2 |reached max iterations\n", - " -5.25 | 0.0000e+00 | 0.0000e+00 | 0 | 11673600 | 0 | 6400 | 5.1 |reached max iterations\n", - "\n", - "Simulation stopped as no error occurred @ EbNo = -5.2 dB.\n", - "\n" - ] - } - ], - "source": [ - "sim_params = {\n", - " \"ebno_db\": np.linspace(-7, -5.25, 10),\n", - " \"cyclic_prefix_length\" : 0,\n", - " \"pilot_ofdm_symbol_indices\" : [2, 11],\n", - " }\n", - "batch_size = 64\n", - "model = LinkModel(DeepMIMO_Sionna_adapter=DeepMIMO_Sionna_adapter,\n", - " carrier_frequency=DeepMIMO_params['scenario_params']['carrier_freq'],\n", - " cyclic_prefix_length=sim_params[\"cyclic_prefix_length\"],\n", - " pilot_ofdm_symbol_indices=sim_params[\"pilot_ofdm_symbol_indices\"])\n", - "ber, bler = sim_ber(model,\n", - " sim_params[\"ebno_db\"],\n", - " batch_size=batch_size,\n", - " max_mc_iter=100,\n", - " num_target_block_errors=100,\n", - " graph_mode=\"graph\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:49:23.859850Z", - "iopub.status.busy": "2025-03-13T01:49:23.859631Z", - "iopub.status.idle": "2025-03-13T01:49:24.255402Z", - "shell.execute_reply": "2025-03-13T01:49:24.254518Z" - } - }, - "outputs": [ + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:45.903639Z", + "iopub.status.busy": "2025-03-13T01:48:45.903247Z", + "iopub.status.idle": "2025-03-13T01:48:45.914833Z", + "shell.execute_reply": "2025-03-13T01:48:45.913951Z" + }, + "id": "kZ21nyrJtPmJ" + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Load the required Sionna components\n", + "from sionna.phy import Block\n", + "from sionna.phy.mimo import StreamManagement\n", + "from sionna.phy.ofdm import ResourceGrid, ResourceGridMapper, LSChannelEstimator, \\\n", + " LMMSEEqualizer, RZFPrecoder, RemoveNulledSubcarriers\n", + "from sionna.phy.channel import subcarrier_frequencies, ApplyOFDMChannel, \\\n", + " GenerateOFDMChannel, CIRDataset\n", + "from sionna.phy.fec.ldpc import LDPC5GEncoder, LDPC5GDecoder\n", + "from sionna.phy.mapping import BinarySource, Mapper, Demapper\n", + "from sionna.phy.utils import ebnodb2no, sim_ber\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6Ze8zPzztPmJ" + }, + "source": [ + "## Configuration of DeepMIMO" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "12rSAfnUtPmJ" + }, + "source": [ + "DeepMIMO provides multiple [scenarios](https://deepmimo.net/scenarios/) that one can select from. Here, we use the `O1_60` scenario, which is an instance of O1 with the carrier frequency set to 60 GHz.\n", + "\n", + "To use DeepMIMO with Sionna, the DeepMIMO dataset first needs to be generated. The generated DeepMIMO dataset contains channels for different locations of the users and basestations. The layout of the O1 scenario is shown in the figure below.\n", + "\n", + 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0s7CmRmYGeK7OfMpMzTWLDzaP05+azZDl5Nv130acKoDL5hnHSJf0kt47b8wtnzrr34JoE6enppgNKpowSe/fuJaIffvjBrN2+ffuJaOabbxLRZZddxjUJ7r/fpF4yM/leTz71FBElS45W+/Odlozal9JF8iHpYhUkszKLd+zYMZ7RefCgGdO0n1/Lli1Nh+Cm0h1z8oucZpsrmTmtl7Bu/XoeXSXx06dPH7vHSZEemToS+munYQntuHnxP4GNOG9nPczrny09dK2Hknmdm9d2sAqCrbPx/CmQdvysuc433K/QXoAAUPlycnOKeUwdeTRxW5BVRpLXX3/D7F/dLV331N9H46RSy1y7vlaaU8zO5sft2rWr6RdelDaXtOGl/f233e/Dwrk5aNfOne06phflXJUCreIgPaVSj/Ao5x8ZUOouehkJvCeZmXh+f9I/UvKRTt+Sb/4CzkZPnvyyfN7xKzJU0gavvvof82rqZ592Oz6bxp+YXl6lzwG4gGI7lqXJFsiSd+8xSTb/9ddf5r2nNTNmvvmW+X5xv3TG1S1mTeW/lImTJnEe+tVXTfdK7Sup2fcB0tM6MLDwTLl+8o69j2sgDbvxJtPHV3PAn3/+ORFdM3Cg+Zsqnof03bcE8ppn6ae8fOLXbhks331K+HK6+8Cphw+c6tAsfFS/KLsevYEB3uPuvXTkrR3mzt3+ybcx1tRMdy98xwFwJblS46Go4VC/m3z40UdEFBERQUQ9unc3v92wYaOp99a/f3+TbMs7t0y+vPKKKwrN7yqtFVGwYsTSpfzdZNU333Llg7vvKvS+Bennu1U+6zXFm3KQv9GEXML9gOkCv9HoHtHZv1NNllf5yd7FhX4/AgBnkCrHq8+c4WM1nl723yZ07Nq0cZNJ0Pbve75O24kTXJlg7TrO7A4YMMBc7yV7hp06djS1cIofHw5Iv17tX37/2LE8UiXzSJUgdeN+kJqUWg0uLCysmOX41LGvh5QqddS8Q4qs0VKibGu21C04X6/IJ8QH3ccBAApVTU7xAgAAAAAAQLVX3IneQO9HHul29z3Rs+b939q4U1W4JeLjrBvX89n3Hdvi9Zq9exOsKec670REBkZG8jn1LtEREZGBvfs2vtDlW1OyNm44FBuTGB9nNUtuGRUaFOgdERkYERnYMiq0VauwwKCS56AAAAAAAACAi6riU7xaSV/zGckyV6hBET2cNO+lcnPtu1dqXnbFipWmA0EryZnly7yn+QsXEtHAgTyzqX379uZe+oh6m74FcmZFyZMZlNrPQOfUF/SdzG1/+ulJ0ilhJK/bypXmVjt37CCix6VPjM6vz8mRFIun/VykAJn7mXyA1zPzNCf80k/xpV+94vq9FbnmOTJ7dP8Zc41u/3M9sQCgUniHSwlB+XOfv3CRSfEqnVf+xefLCl0V7aL3/vtzOFvWurVd/8jrr7+OO7JIT3HN9Hz+xRcmEfXu7Nl2Y2DxdK7oDUOH8szNwYPNuhWcg6ldzHXW/NatW4mogzzKg+PH81xLn/MlE3Uu6gLJb+lsdB3NbJcZ3IbntqfFn5Xe4TzSJkjHPk32+DcIKHEeqC45/SSPlie3n5BxPt+MeLUvro25nwBVyE1ydfr3ePjQ4UJXJOn0aU7MfP01VyOQrnhpafwX/ehjj5nbZGZkSlUAniH+yROYCAYAACAASURBVEeLzZ6eqlunjtQe4B7hDz70kHS95dHmL8njat/c6dOm2o1RBWlXvyef5OoIU6bw7Rs35lagv//+u1zzil26VOfLK3ebccZLzrL41+XHPXssVWoMnDX5xcCmQRc0w10TPAk/nzLjm36mBDap5eRpHq1O8dxzzxNRO/nUCAjgbfLZMv7U6yyp6Nw8fnaTJj0jqdAu3BtMalcU3+3YdemrZZUkh37fsc2Fn5a/hQ+l41p0dLT51jBnzhxzy8REvk3nTtwx+plJnMNoLXsIKqoV7zm0a9vO9KjWbzGan+4peyB3Sy6t+HdOq1atpDv1B/x6LfmMv39t3877GBbex3jn7Vl22Th9lPwCcTxN1gY1q2U+33PScsz7uU50Pcc/o3cfODVuT3zHFnWevLlzZP0g218FBlgmPtR9dFLagnUxq3ccqZJ3x8b1h2fP2lHUb+PjrPFxHLDeLieAp0+x9O7TZOy4Lo6ckY2Psy5e+OuK5bEFfxUbk2j738Agy+oNt5f2GZRefJx1+7Z4a0qWnt7evi2+a3TEjLcHVNTjgetLlK66X6/iHvnNpUqQ1hiw/dxPSeZPzIYXcT22N159zewh6Kiox5H0qIv2IA+RjrmHj/AIcHGLi+UIzGOl2FKhofzdpGOHDhd0L78I/szyCuA9hOxUPt515k8+DuMT6mt+67hsmcNx+rfzZeo9pSJCQJOgC1oOADgD3btbKnu/Bw5w1TQ/P1+pZ/OqGXM2b97Ce86xvOf8jKRsb7nlZrt11+puu6TLeFOp0RITE8P33cK1Ld+ZZb9vZkuPFw0eMkT2JHnnYceOneb3WXKUPlr2w23rJBXFUttbMrs+cuyaK8qkHObj2AGNeTabT7h9xrd4euz69C+nzBEhVatZybXoAABqJqR4AQAAAAAAwPX8vO/kVQPnDR3UZvy9lzZo8I9jf3WD/Z68qfOoflFVeKLXQdaUrBXLYzduOPTSlF5doyOKudOK5bGzZ+0waeDiFb+oingWGzccWrE81u5MMwAAAAAAAFSQKj7F6+7PK5B8iL8E/v7HH5xLa9PG7jb7/vzTzMfs0a0bzxz/jGeOb9q00WQvtGvLPXffTUR33TWa54YH8XzGZOky1b5tWyJaJPPfD73womRH3E0Gd8ITT9h1LyiKdspcLqk47WK1a9cu7k8gqbUrZd2OS4eYWW/NMnmRZUvt03idO/Ms+/r16xPRT9Lb4PgJzplZ6nqb2+g8eu09mXxQ08Z8faLMcPfpw3Og3C2O9pfSGfRWSQOnnTyfPNYOMR6+6EkJUHm0I6N3LW/p7bRUqghwN7vbbrvNLnlzoWyrEXSQmeaavi2dunXrmsvi6XjVuUtnc1kUHfHmvD/XzGovWEXAIvmVsE7h3GPmv5yHy8/JNz/XklExpDX3znQv0E9XO10lx3I/m6S9p801KlDmuftFlpwDBoCKo7kTDwv//W6TuibaVbRhw4bn/1plL27oDTeYy4L0r1grGRT8i35g3APmt1q1RftdaeJWZ8cXNavdVrBkgN55520z4173qW6QtSrYhXfZMu5LqsFMS5C33W9D2oeZPbF8qUlzaifvAVIuLzOwRckZ3BxJAp34v2PSm+p8F95akt/1Di0ukewMNE01ePAg0xdWXwXtvHvTzbdwN1npODteXsEnJ0yQpG+Akz+v0tH9eG95xfdIt/vYWM5ltpVvLkp7RT8kSfTSuf3228w7X9+9GfKNxt3Dw9T5cIS+M6Ol224XyVtrZY6Cf0d6/SpJ4ymfkPPpDV1OYNNapkuldlnTRHueJNTDutQ1+wlFOXuUI7Cnf+VvkZlnMj+ev/Pj+TtvvLHd4xN6RNT7R1NhJznRqwndiMhz62ZNyYqPs+7YFr/9f2Wc9cqHH1jz0pReRdVtXrE8dvorP9pd2TU6omVUaGCgtxaFjvtfPlirQFfYE7K3eOGvixftcfDcM9Qotrn8UwmFl08PqsVjwrBhw8xlQR42o5btZ6VeP+utNyUJ52c+98+ePWs+qfVzX295oXpItYPLLrus0PvpYynbZ6oVC0Lb8Tea4z/yn3meZNFO/MSf4KGyPxDYrOTPfe24f+yHOLsuvLVb8qeDpz8yGwDOxfYvOiMjs9B108o0/7rmGiK65uqrC71Nv379zC1r165tt+S7Rt9JROPu5+65x6SGUILUQmjerBkRPSz7jbVqFZd5DQ/n0ekbm721ggouQWtwnuNuM+J58ogXIvXYjm2NM0ncY1t59IvoGWkyvsXT70eJO7nTcGpcqrmtTxjf1xcVKAEAioA9QgAAAAAAAHBtS5fuWb398JCrWz049vK6wX62z0VP9A7s0mjBupjdB6qgR+/wkW0LvTI+zjp71g7t2qumT/mha3REwYrNsTGJtud3A4Msw0e0GzSkpe0t9VG0DfCK5bGVk+KNjUmcNuWHQpO7+kRaoiUwAAAAAABAxXCLGuVoX8aKkGPlGUCHVx2UTlHc4Wn92rVmPpHSmeB6WZDOY9Kekcp2ZpPOVdf76vXa40qv11mcOqvdkURXvk0XXlt6X11aUbexpWurazJauhos+5yTwfW78cwm/4vOJxW0v1rCLj4MkbwvyVzvHcwzmOpeXl+6uxW+/rom+ZILSd7L903U3i3SEktnlTYcyDPEPYudIw8AFSEjkTM0cRu4K6S3l8X0OLl/LM/ErFevXjVImuoolJSUJN375nNm6/U3eDZ6Do9+9bvziOdbz6+Y+6bEciWGhF9s+k0K7eLpHcjbzUtiK9lneZmZyZl2t9QsXS1JC4V2qmNmmAJA1ToTc9p04rzu2muJaLYkZXUP0LVGP60K8/Y773CX2edf4D2rIJ5DGdmHc8lunvb7pamHOK14Ypt07bUZrnxkbntg4yDJ43Lq0UP21rSHX9oxTiOlHEo28+LP3UuSu/V7RJqe5RWtyVl+rL2pg2X1C3/E1x7vTkRDeje3u371mjWmU3LHjh3N9dpJccSIkUR0xx3DTX0d3Wc+fOgQd2mVqhKa0CroizVc9efxmT8U+lvPHN56Pj+/TFLeoRK2kuOSJQ9xTL4XaDWjTz/+iHMYzZuXOnNW+c69tyUrP2PGDO5UPW36+b+F/g0L/bvOSuFP7WObOe2RbT3/umgSzl+yGhb51uPhxdshOy3L/C1kaVrU5i/It66f+VsICvQe1q35sO7N/X0K+Zrzy8GEUpzoHdUvamS/KMdvv3jhr7a9eH/YfmcxN57+yo+2vXXHjutS8JTwsxM3mTPBgUGWmbMGtowKdXx9KkjBwtGBQdxXuFffxmU/wfzIe1uq5Hw8lK+sZH57HF3HGfoWTVsQ0YZ1fMynjnwWuC6tQdLzql6c003kmhwNr24sNdLOfzLq2Ji4gxNpZ/afsXuuflLbyb9hoPk01+84mWd4bEz9m+P4afGppqKbqtWstlQ7qGNuDwDOQ6uIHf7yAPfHbdzUjHhax9F15cq++iPSH/3d9+bIEewIOYJ9vnSKHodJlO93Z2LPH8HW/Todu/Sbjh7H1oM1etRa9+70G6Ie1VGeMqJG9m1YYn0XAICaDEe6AQAAAAAAoJpITc9esC7mlimrF66LOZthf1K/fdOwN8Z0n3Ffjw7Nwp3k+WoZZ/PfHTbVm5UGc81/h49o5wzndzeuPzz9lR9tz+8OH9l26fIbJzx9RSW3AQYAAAAAAKiZqrhQs0cAr0Dt5tznLCZmL3cUuPseInr99dd4xlOTJmb+vm1O13G2+VqlvXtLR5dWfOc2R25zXDolTH75ZZPf9ZMcm299+1yCzsoMaRsqczm5C0vGqXTTfe3vNfw9P6ABp3796/GlJZTTbHlpnCG2xumsz7OSb7OZFy9pkjqX1UN+F6AK+UhCS7uSnNrOM7tnvf2O5MBm8wxH6UPpVaDLo2vJkWRSckrK+Y590oNT53sWld9V5/qRt6xtRqrEPQkm8aPzQzNkdmdGcuEdbrwk4xt6SaiZHY957gDOo1Yr3vdLl72ar77+moh+++03k92sX6+uS2R5E6U77/ebNxPR7t2/SI6W9znDo+vZ5XeVPqMA6QuuI1LCzzzPPSeD58VnnM4wlyWQBQdIU8+wzuGVlt8tLzukB7Nth13NOWk/Zk2vHjhwQK7n377/PmcFJk2aVEyK13UFybeGjDzee//999+5tu2l3PFx4ID+NpWNnP1vQasTbdm6hYgOHuTUtZcff3bXubS4qiTarzriqov4b0H6rp09lmryHKlHpQfb0dRC76vO5UKaSp/+9qF2tTr0RO+yrfsLTfTqid5fDiZMXbLjeFKa3ZLHXd9u1so9ZdsqF0CTrybIu3dvgt199/6zDPKgIS0rbd2KwoWjp5zPzQcGWV6a0gtndqEgSy3eJw+SvrOxe/lN3qt3H9OHMiQk2OW22ekkzuOuXLmSazLFx5sjNrb5XaWjX2in85/USXtPm2oc2ptfL4unY12Q5nc7hOF7DYDT0v3/Wi34r3V/zH4iuunmW4jopRe50k9kZKTLvXRapXL2u+8S0Xtz3jeVh3zqFH4EO7R9uPlZs7y6X3fmzyRzqWOauwfvs+VmSxXMfPvH9a7tLXuS9ZHfBQAoEVK8AAAAAAAAUA2ZRO/nW/cXfHbtm4Z9MnHgUzd3rvfP3r03dGtud01Fi4g8X+rQNher4mXyrlHlrW2tKVnTpvxg1lMLR+P8LgAAAAAAQCWr4in/OqsxpB3PecxOzzY9unb34STEFZdfznP5mzXjOY+ertGJqnjxcTzHc+sPPN/5wMGDpudKna71iukQ6eHtYXJvmvawHkkxM6GsR6zmsng67ymsI3dt8Yv0d4otAlCz+db1N51FzvVbkh4kGRmST80rPJ/qMmTY1rSu1ioIkDRtwRnuRdHPCH+pVeBbh3PPacd5nvtZyfRkp2aZ3lRuMnx6SmbIP5Jv7xfhb8ZPAHA2+tdd93Kel530O6fT/j58VHo7vedyr5W7xcOMPKHtOFtjkVnnRTmX5dWeuzILPkn6TqXL+JadVninWJ3t7l2bbx/UvJbpZeWKOZ4Hxj9YzG9fl06utiwWL5PirX70/RBu4feMtzvnuc9k8qf/yhUrC4s0VBJ3rYGkby5ZCU1aFxknlus1xR4k/e+DW4dKRY2SUxf6DaVe9wizL2SVftXZcvowJz3HVO/wkL813YvQ71CB8lg+YT4l5v5T07NnrdyzbMv+Uf2jBnRuZPfbAZ0bDejcaM3OIwvWxhxPStMCzqP6R01dsrNKtr/t6d5CWVOyqvYs74rlsbH/CxY7T2NgcGb6+agOypGQGW++6dKvmI54wW34nR/Sprj3v5sk1YIvCTGV21IOJJvvfTrKFbJ8i7vZTwhupffFMRwAZ6d7Izoy6F/3th3biaj/wKurwWt3Llkr9YqKOtKi31lC2oeZvO9p7bAr1SjP7VXK0WzNB9vScTVA+vvqNvR0+NgRAEBNhhQvAAAAAAAAVHPHk9KmLtl565TVa3YeKfhMB3RupInekABv/W/ziFqVtkFibYoztypwutTupO/GDYcqa70KER9nXbzofBXrseO64PwuAAAAAABAlXCK6TCafqgrvZrOBHFd/lMHOav6xfLlTrB25c/di8+sBzWuZbpGafKseNq7pU50Xem8y/M3E//gqdPZVimQVcQkf50D5R8RILPpQ0x/SgBwHvp3qv1a9FLTKtWEhGrK3lNTPyk0B6zzOs9tJb2UHJtmecnp+3cCgNI9Ip3lrV36slKK26txTp7+ntJr0LsUmVovyeGFy95d7lme6Z+pHcezuUBBXiZfuvvx6OfpLY8S7F1M3Rfn5yHZ0LdmziSiFhe3kI+I4rZYnhRqWL16dbX/i3GXT67aXl6mO2+W9LCvqj+F5pdF8/vNx8d00z+RyN/OEkM4eUZuha+X5ms9fD1K8bmvfzuBjYLMp3yuZF9y0nLOd/S3eJq/ODcZPUq3d6EnehesjSkq0du7/UX681M3d7n7jQ2leIgLFR9n3bj+sLlTrz5N7BZgVwN58cJfe/dpUlVB3hXLY02J5q7REc7QGBicnyZZQzuEmyoUOWcLT6+6wpMhqUBgkc9xOZLjyFgkt/EJ47pE3lKHICc1xxzPyZWu/CTVidx93GWss5hOxui8C+Ba9DuO7uHrvo31IO9BZaXaN2Jwfvq9I7ARf1Pza+DvYLL2XD02OXqjdd0yEjjFm3EinauyneSqbLqb6+HlYT4XvKUui34/KvsRJACAmgMpXgAAAAAAAKhB9ETvI+9t+eVggt2z9vrfLIpm9WuN6hdV0dvEmpL1zMRN5r9doyN6921sd5vAIIvtmdT4OOvoEStNqeRKtmJ5rHnA20e2tXsu27fFb98WX1XrBgAAAAAAUKM4UVF7TWhpkiOwSZBJcuSl51aHTJtMP3KXrJ52nPKWHMaFps109muAbB8/6U+ZkZBu5sDqbHedY+Up80ktOre0FpK7AK4EM7VLoMlgD239h20F4Np0jrZWGamZtUZ0C3jK/qFeVleNG3Fisn27dlyZpk4dB59l/Xpc6cfTs6b04tJEr49HVfaSDw0O5m8r/v6mU1p6NmcvzoZIyty9Yr+X6V6Qp7+Xuaw4uw+cevjAqQ7Nwkf1i2rfNKzg44zsF7X19/j98ckVtArbt8XPnrXDtq/tS1N6FXrL4SPbbtxwyMRn9SzvoCEth49sW2Lv3nK0cf1hsw4RkYEaL+bSzQt/3b4tPj7OavtQvfs27oKYL9jQzzvvYB+5rLlb5n97Pl4O9iwHAFfkLsdv/SL8zWXNpEf7bbdDCBWyxwUAAKWGvuUAAAAAAABQQ5kTvWP/1fbiBrXtNsLkkZffPaOs5Zq3b4u3/a81JYuLM284ZJt2bRkVOnlKr6LKL0dEBs6cNfChcavNGVYN1K5YHtu7b+NefZoUzP5WhB02T6R338bWlKzFi/YsXvhroQ+1cf3hjesPr1ge++TEK9GvFwAAAAAAoNw56SlerbzvVUXthVyFdnfwq19z54IBAAAAgKto0aJFKda0devWeIWhEqSmZ0WGFfLFqm6w3+SRl+8+cKosq/DwA2uK+W1EZODwkW1LTLu2jAqdOWvgMxM32YVl9Uzq7FmBg4a0HDSkZYX26LU9Vx0RGfjQuNUl1mSOjUl8aNzqmbMG4iwvAAAAAABA+UKKFwAAAAAAAGquDs3CJ4+8zN+n8Iqp7ZuGhQYFVNzGiZRKy9aUrBLPzraMCl365bDFC39dvGiPbZxXqyXPnrVj8aI9w0e0G/7PFrnlRcPHZmGLF/6q/9VT1F2jI7RkdGxM4vZt8bZrqP2GP1h0fYWefgYAAAAAAKhpcIoXAAAAAADKzR/7ONUX5Ft553L+OHAaLx+UWr1gv4FdGi7bsn/3wQSN85rmu/WC/eqFcLR3VP/ODSqsc9z2bfHalHfCxCsdqbeskV+t0myX6LWmZM2etWPjhkMVURt5794E2//qQ48d18XujHLLqNCWUaGDhrR8duImk/rVM9ATnr6ifFcJAAAAAACgJsMpXgAAAAAAAKihjielTV2ys9Dnfjwp7XhSGvfr3X+kfZOoUm+fH7bfaftfa0rW3r0JsTGJtu14rSlZz3IRZvszpoUKDLIMH9l2+Mi2G9cf3rTh0Mb1h21vVUG1ke1yw0Q04ekriqovHRhkeWlKL9tKziuWxw4f2VaTvgAAAAAAAFB2OMULAAAuIyf9Yr5M7YaXrIzcPDnxZqm9kn92y3Pp5wIAlSnfgceat+IPIvpALiuHI2tF5GZz6TwcW3eoXgKDLF2jI7pGRwwf2Xb7tvjpr/xowrizZ+0IDLKU2JfX6N23ce++jceOsy5e+OuK5bHm+oqojWyXGO7dt3Hx6xkYZBk7rottH+KN6w9XUBFpAAAAAACAGsgdLzoAAAAAAABA5esaHfHBoutt47azZ+0omJctXkRk4ISnr5jx9gDbE7rxcVbbk77lbuy4LiUu0jToVTv+V7cZAAAAAAAAyg4pXgAAcB35XryqeQGyws6WxHIpeRmyttiGAHBh0vKCJXla8jxR50mn5rp7E1FW81v4P/lOs155fA7P68gqHotzUp1ghaDKBAZZnpx45egRK3UFrClZWtP4Qtena3TEzFkDHxq32pwhXrxoT8WlZh0sudw1OsKcaY77Zw4YCsrNrscDVR7KWZeVuzuPq+5ex1z7aQCA08jP5+/O+bm18ZKUCzcPq1QUy6kGzwUALlRudn3Z4w3AljM8LPtlVCzN8QqkeAEAAAAAAACqTMuo0K7REebRS512bRkVOnxEO/Nfa0rW9vILzpau5rPtmeB4nOIFAAAAAAAoP0jxAgAAAACAQ7LyfeRmrjRPNN+dv/JkBTtZB9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+ "\n", + "In this example, we generate a dataset that consists of channels for the links from the basestation 6 to the users located on the rows 400 to 450. Each of these rows consists of 181 user locations, resulting in $51 \\times 181 = 9231$ basestation-user channels.\n", + "\n", + "The antenna arrays in the DeepMIMO dataset are defined as number of horizontal & vertical elements. In the following example, a single-user MISO downlink is considered. The basestation is equipped with a uniform linear array of 16 elements, which is oriented towards the positive x-axis by default. The users are each equipped with a single antenna. These parameters can be configured using the code below (for more information about the DeepMIMO parameters, please check [the DeepMIMO documentation](https://deepmimo.net/documentation)." + ] + }, + { + "cell_type": "code", + "source": [ + "# Import DeepMIMO (install package if import fails)\n", + "try:\n", + " import deepmimo as dm\n", + "except ImportError as e:\n", + " import os\n", + " os.system(\"!pip install deepmimo\")\n", + " import deepmimo as dm" + ], + "metadata": { + "id": "Y39zbDbyuQ2J" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:45.918949Z", + "iopub.status.busy": "2025-03-13T01:48:45.918713Z", + "iopub.status.idle": "2025-03-13T01:48:52.133244Z", + "shell.execute_reply": "2025-03-13T01:48:52.132383Z" + }, + "id": "h8cNH5cGtPmN", + "outputId": "edf9507e-66a9-47f8-cd14-e3f977c3f0d0", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading scenario 'o1_60'\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Downloading: 100%|██████████| 2.30G/2.30G [00:45<00:00, 54.6MB/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "✓ Downloaded to /content/deepmimo_scenarios/o1_60_downloaded.zip\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Extracting: 100%|██████████| 4161/4161 [02:37<00:00, 26.34file/s] \n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "✓ Unzipped and moved to /content/deepmimo_scenarios\n", + "✓ Scenario 'o1_60' ready to use!\n", + "Loading TXRX PAIR: TXset 6 (tx_idx 0) & RXset 0 (rx_idxs 497931)\n" + ] + } + ], + "source": [ + "# Download the dataset\n", + "dm.download('O1_60')\n", + "\n", + "# Load a BS-RX grid combination\n", + "load_params = {'max_paths': 10, 'tx_sets': [6], 'rx_sets': [0]}\n", + "dataset = dm.load('O1_60', **load_params)\n", + "\n", + "# Select a subset of users in the dataset (and trim matrices)\n", + "sel_usr_idxs = dataset.get_row_idxs(range(400, 451))\n", + "dataset_t = dataset.subset(sel_usr_idxs)" + ] + }, + { + "cell_type": "code", + "source": [ + "# Channel generation\n", + "params = dm.ChannelParameters() # Load the default parameters\n", + "\n", + "# Configuration of the antenna arrays\n", + "params.bs_antenna.shape = [16, 1] # BS antenna shape [horizontal, vertical]\n", + "params.ue_antenna.shape = [1, 1] # UE antenna shape [horizontal, vertical]\n", + "\n", + "# Configure time domain channels. Sionna will generate in frequency domain\n", + "params.freq_domain = False\n", + "\n", + "# Generates a DeepMIMO dataset\n", + "dataset_t.compute_channels(params)\n", + "dataset_t.channels.shape # (num_rx, num_rx_ant, num_tx_ant, num_time)" + ], + "metadata": { + "id": "XkPe8gvwuPfk", + "outputId": "fe0fe508-7a82-4639-d46c-86c5b0be6967", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Generating channels: 100%|██████████| 9231/9231 [00:00<00:00, 29525.95it/s]\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(9231, 1, 16, 10)" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dhOdsh0rtPmN" + }, + "source": [ + "### Visualization of the dataset\n", + "\n", + "To provide a better understanding of the user and basestation locations, we next visualize the locations of the users, highlighting the first active row of users (row 400), and basestation 6." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:52.136681Z", + "iopub.status.busy": "2025-03-13T01:48:52.136424Z", + "iopub.status.idle": "2025-03-13T01:48:52.372533Z", + "shell.execute_reply": "2025-03-13T01:48:52.371889Z" + }, + "id": "0ShXyVmItPmN", + "outputId": "0b9b8a8c-4076-408e-9027-cf0160c3a797", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 388 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + " # Plot the azimuth of the AoA\n", + "dataset_t.aoa_az.plot(cbar_title='Angle of Arrival (deg)')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YlwUGc-NtPmO" + }, + "source": [ + "## Using DeepMIMO with Sionna\n", + "\n", + "The DeepMIMO Python package provides [a Sionna-compliant channel impulse response generator](https://nvlabs.github.io/sionna/phy/tutorials/CIR_Dataset.html#Generators) that adapts the structure of the DeepMIMO dataset to be consistent with Sionna.\n", + "\n", + "An adapter is instantiated for a given DeepMIMO dataset. The adapter only requires the dataset to adapt and it will generate samples for all users and basestations in that dataset.\n", + "\n", + "In this example, `SionnaAdapter(dataset)` will return the channels from the basestation 6 and the 9231 available user locations.\n", + "\n", + "**Note:** The adapter assumes basestations are transmitters and users are receivers. Uplink channels can be obtained using (transpose) reciprocity." + ] + }, { - "data": { - "text/plain": [ - "[]" + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:52.376377Z", + "iopub.status.busy": "2025-03-13T01:48:52.376179Z", + "iopub.status.idle": "2025-03-13T01:48:52.380757Z", + "shell.execute_reply": "2025-03-13T01:48:52.380192Z" + }, + "id": "6LDSxX9PtPmO" + }, + "outputs": [], + "source": [ + "from deepmimo.integrations import SionnaAdapter\n", + "\n", + "deepmimo_sionna_adapter = SionnaAdapter(dataset_t)" ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": { + "id": "rl8wZ1O-tPmO" + }, + "source": [ + "## Link-level Simulations using Sionna and DeepMIMO\n", + "\n", + "In the following cell, we define a Sionna model implementing the end-to-end link.\n", + "\n", + "**Note:** The Sionna CIRDataset object shuffles the DeepMIMO channels provided by the adapter. Therefore, channel samples are passed through the model in a random order." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:52.384454Z", + "iopub.status.busy": "2025-03-13T01:48:52.384238Z", + "iopub.status.idle": "2025-03-13T01:48:52.397002Z", + "shell.execute_reply": "2025-03-13T01:48:52.396376Z" + }, + "id": "EeoY1rP9tPmO" + }, + "outputs": [], + "source": [ + "class LinkModel(Block):\n", + " def __init__(self,\n", + " DeepMIMO_Sionna_adapter,\n", + " carrier_frequency,\n", + " cyclic_prefix_length,\n", + " pilot_ofdm_symbol_indices,\n", + " subcarrier_spacing = 60e3,\n", + " batch_size = 64\n", + " ):\n", + " super().__init__()\n", + "\n", + " self._batch_size = batch_size\n", + " self._cyclic_prefix_length = cyclic_prefix_length\n", + " self._pilot_ofdm_symbol_indices = pilot_ofdm_symbol_indices\n", + "\n", + " # CIRDataset to parse the dataset\n", + " self._CIR = CIRDataset(DeepMIMO_Sionna_adapter,\n", + " self._batch_size,\n", + " DeepMIMO_Sionna_adapter.num_rx,\n", + " DeepMIMO_Sionna_adapter.num_rx_ant,\n", + " DeepMIMO_Sionna_adapter.num_tx,\n", + " DeepMIMO_Sionna_adapter.num_tx_ant,\n", + " DeepMIMO_Sionna_adapter.num_paths,\n", + " DeepMIMO_Sionna_adapter.num_time_steps)\n", + "\n", + " # System parameters\n", + " self._carrier_frequency = carrier_frequency\n", + " self._subcarrier_spacing = subcarrier_spacing\n", + " self._fft_size = 76\n", + " self._num_ofdm_symbols = 14\n", + " self._num_streams_per_tx = DeepMIMO_Sionna_adapter.num_rx\n", + " self._dc_null = False\n", + " self._num_guard_carriers = [0, 0]\n", + " self._pilot_pattern = \"kronecker\"\n", + " self._pilot_ofdm_symbol_indices = pilot_ofdm_symbol_indices\n", + " self._num_bits_per_symbol = 4\n", + " self._coderate = 0.5\n", + "\n", + " # Setup the OFDM resource grid and stream management\n", + " self._sm = StreamManagement(np.ones([DeepMIMO_Sionna_adapter.num_rx, 1], int), self._num_streams_per_tx)\n", + " self._rg = ResourceGrid(num_ofdm_symbols=self._num_ofdm_symbols,\n", + " fft_size=self._fft_size,\n", + " subcarrier_spacing = self._subcarrier_spacing,\n", + " num_tx=DeepMIMO_Sionna_adapter.num_tx,\n", + " num_streams_per_tx=self._num_streams_per_tx,\n", + " cyclic_prefix_length=self._cyclic_prefix_length,\n", + " num_guard_carriers=self._num_guard_carriers,\n", + " dc_null=self._dc_null,\n", + " pilot_pattern=self._pilot_pattern,\n", + " pilot_ofdm_symbol_indices=self._pilot_ofdm_symbol_indices)\n", + "\n", + " # Components forming the link\n", + "\n", + " # Codeword length\n", + " self._n = int(self._rg.num_data_symbols * self._num_bits_per_symbol)\n", + " # Number of information bits per codeword\n", + " self._k = int(self._n * self._coderate)\n", + "\n", + " # OFDM channel\n", + " self._frequencies = subcarrier_frequencies(self._rg.fft_size, self._rg.subcarrier_spacing)\n", + " self._ofdm_channel = GenerateOFDMChannel(self._CIR, self._rg, normalize_channel=True)\n", + " self._channel_freq = ApplyOFDMChannel(add_awgn=True)\n", + "\n", + " # Transmitter\n", + " self._binary_source = BinarySource()\n", + " self._encoder = LDPC5GEncoder(self._k, self._n)\n", + " self._mapper = Mapper(\"qam\", self._num_bits_per_symbol)\n", + " self._rg_mapper = ResourceGridMapper(self._rg)\n", + " self._zf_precoder = RZFPrecoder(self._rg, self._sm, return_effective_channel=True)\n", + "\n", + " # Receiver\n", + " self._ls_est = LSChannelEstimator(self._rg, interpolation_type=\"lin_time_avg\")\n", + " self._lmmse_equ = LMMSEEqualizer(self._rg, self._sm)\n", + " self._demapper = Demapper(\"app\", \"qam\", self._num_bits_per_symbol)\n", + " self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n", + " self._remove_nulled_scs = RemoveNulledSubcarriers(self._rg)\n", + "\n", + " def call(self, batch_size, ebno_db):\n", + "\n", + " # Transmitter\n", + " b = self._binary_source([self._batch_size, 1, self._num_streams_per_tx, self._k])\n", + " c = self._encoder(b)\n", + " x = self._mapper(c)\n", + " x_rg = self._rg_mapper(x)\n", + " # Generate the OFDM channel\n", + " h_freq = self._ofdm_channel()\n", + " # Precoding\n", + " x_rg, g = self._zf_precoder(x_rg, h_freq)\n", + "\n", + " # Apply OFDM channel\n", + " no = ebnodb2no(ebno_db, self._num_bits_per_symbol, self._coderate, self._rg)\n", + " y = self._channel_freq(x_rg, h_freq, no)\n", + "\n", + " # Receiver\n", + " h_hat, err_var = self._ls_est (y, no)\n", + " x_hat, no_eff = self._lmmse_equ(y, h_hat, err_var, no)\n", + " llr = self._demapper(x_hat, no_eff)\n", + " b_hat = self._decoder(llr)\n", + "\n", + " return b, b_hat" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "15OdNJoLtPmO" + }, + "source": [ + "We next evaluate the setup with different $E_b/N_0$ values to obtain BLER curves." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:52.400127Z", + "iopub.status.busy": "2025-03-13T01:48:52.399905Z", + "iopub.status.idle": "2025-03-13T01:49:23.855650Z", + "shell.execute_reply": "2025-03-13T01:49:23.854765Z" + }, + "id": "WC1tSAq9tPmP", + "outputId": "26a07782-c588-4a32-c706-364f58b3dfe6", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " -7.0 | 1.2781e-01 | 1.0000e+00 | 29840 | 233472 | 128 | 128 | 7.4 |reached target block errors\n", + " -6.806 | 9.7352e-02 | 1.0000e+00 | 22729 | 233472 | 128 | 128 | 1.0 |reached target block errors\n", + " -6.611 | 6.6376e-02 | 9.4531e-01 | 15497 | 233472 | 121 | 128 | 1.0 |reached target block errors\n", + " -6.417 | 3.0492e-02 | 7.9688e-01 | 7119 | 233472 | 102 | 128 | 1.0 |reached target block errors\n", + " -6.222 | 1.1498e-02 | 4.7656e-01 | 5369 | 466944 | 122 | 256 | 2.1 |reached target block errors\n", + " -6.028 | 1.4813e-03 | 1.3281e-01 | 2075 | 1400832 | 102 | 768 | 6.8 |reached target block errors\n", + " -5.833 | 1.6177e-04 | 2.2645e-02 | 1303 | 8054784 | 100 | 4416 | 38.1 |reached target block errors\n", + " -5.639 | 1.4563e-05 | 3.9062e-03 | 170 | 11673600 | 25 | 6400 | 56.0 |reached max iterations\n", + " -5.444 | 3.5979e-06 | 6.2500e-04 | 42 | 11673600 | 4 | 6400 | 54.6 |reached max iterations\n", + " -5.25 | 0.0000e+00 | 0.0000e+00 | 0 | 11673600 | 0 | 6400 | 57.5 |reached max iterations\n", + "\n", + "Simulation stopped as no error occurred @ EbNo = -5.2 dB.\n", + "\n" + ] + } + ], + "source": [ + "sim_params = {\"ebno_db\": np.linspace(-7, -5.25, 10),\n", + " \"cyclic_prefix_length\" : 0,\n", + " \"pilot_ofdm_symbol_indices\" : [2, 11]}\n", + "\n", + "batch_size = 64\n", + "model = LinkModel(DeepMIMO_Sionna_adapter=deepmimo_sionna_adapter,\n", + " carrier_frequency=dataset_t.rt_params.frequency,\n", + " cyclic_prefix_length=sim_params[\"cyclic_prefix_length\"],\n", + " pilot_ofdm_symbol_indices=sim_params[\"pilot_ofdm_symbol_indices\"])\n", + "\n", + "ber, bler = sim_ber(model,\n", + " sim_params[\"ebno_db\"],\n", + " batch_size=batch_size,\n", + " max_mc_iter=100,\n", + " num_target_block_errors=100,\n", + " graph_mode=\"graph\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:49:23.859850Z", + "iopub.status.busy": "2025-03-13T01:49:23.859631Z", + "iopub.status.idle": "2025-03-13T01:49:24.255402Z", + "shell.execute_reply": "2025-03-13T01:49:24.254518Z" + }, + "id": "q5G8_EU6tPmP", + "outputId": "8c3bffcc-d47d-4cde-9c65-0f6c984ea3cb", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 716 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[]" + ] + }, + "metadata": {}, + "execution_count": 11 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "plt.figure(figsize=(12,8))\n", + "plt.xlabel(r\"$E_b/N_0$ (dB)\")\n", + "plt.ylabel(\"BLER\")\n", + "plt.grid(which=\"both\")\n", + "plt.semilogy(sim_params[\"ebno_db\"], bler)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MY82CFF9tPmP" + }, + "source": [ + "## DeepMIMO License and Citation\n", + "\n", + "A. Alkhateeb, “[DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://arxiv.org/pdf/1902.06435.pdf),” in Proc. of Information Theory and Applications Workshop (ITA), San Diego, CA, Feb. 2019.\n", + "\n", + "To use the DeepMIMO dataset, please check the license information [here](https://deepmimo.net/license/)." ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "plt.figure(figsize=(12,8))\n", - "plt.xlabel(r\"$E_b/N_0$ (dB)\")\n", - "plt.ylabel(\"BLER\")\n", - "plt.grid(which=\"both\")\n", - "plt.semilogy(sim_params[\"ebno_db\"], bler)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## DeepMIMO License and Citation\n", - "\n", - "A. Alkhateeb, “[DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://arxiv.org/pdf/1902.06435.pdf),” in Proc. of Information Theory and Applications Workshop (ITA), San Diego, CA, Feb. 2019.\n", - "\n", - "To use the DeepMIMO dataset, please check the license information [here](https://deepmimo.net/license/)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + }, + "colab": { + "provenance": [] + } }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.16" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From 8ca194f50816c2b857728778087042b896135a43 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Morais?= <19170303+jmoraispk@users.noreply.github.com> Date: Sun, 29 Jun 2025 13:15:53 -0400 Subject: [PATCH 2/7] replace raw image with link --- tutorials/phy/DeepMIMO.ipynb | 334 +++++++++++++++++------------------ 1 file changed, 164 insertions(+), 170 deletions(-) diff --git a/tutorials/phy/DeepMIMO.ipynb b/tutorials/phy/DeepMIMO.ipynb index ee8fc242e..f3de4242a 100644 --- a/tutorials/phy/DeepMIMO.ipynb +++ b/tutorials/phy/DeepMIMO.ipynb @@ -3,7 +3,7 @@ { "cell_type": "markdown", "metadata": { - "id": "lSbcn0aLtPmH" + "id": "tp7_gHACpeS4" }, "source": [ "# Using the DeepMIMO Dataset with Sionna\n", @@ -14,7 +14,7 @@ { "cell_type": "markdown", "metadata": { - "id": "Thv9PInhtPmI" + "id": "SpgUmMabpeS-" }, "source": [ "[DeepMIMO](https://deepmimo.net/) is a generic dataset that enables a wide range of machine/deep learning applications for MIMO systems. It takes as input a set of parameters (such as antenna array configurations and time-domain/OFDM parameters) and generates MIMO channel realizations, corresponding locations, angles of arrival/departure, etc., based on these parameters and on a ray-tracing scenario selected [from those available in DeepMIMO](https://deepmimo.net/scenarios/).\n", @@ -30,7 +30,7 @@ { "cell_type": "markdown", "metadata": { - "id": "G-qy_3RatPmI" + "id": "70aKlvi6peTA" }, "source": [ "## GPU Configuration and Imports" @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-03-13T01:48:42.830858Z", @@ -46,7 +46,7 @@ "iopub.status.idle": "2025-03-13T01:48:45.899584Z", "shell.execute_reply": "2025-03-13T01:48:45.898771Z" }, - "id": "yuejsob6tPmJ" + "id": "p-UvC0UUpeTC" }, "outputs": [], "source": [ @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-03-13T01:48:45.903639Z", @@ -94,7 +94,7 @@ "iopub.status.idle": "2025-03-13T01:48:45.914833Z", "shell.execute_reply": "2025-03-13T01:48:45.913951Z" }, - "id": "kZ21nyrJtPmJ" + "id": "_lwRzt-mpeTE" }, "outputs": [], "source": [ @@ -117,7 +117,7 @@ { "cell_type": "markdown", "metadata": { - "id": "6Ze8zPzztPmJ" + "id": "StLgR-2PpeTF" }, "source": [ "## Configuration of DeepMIMO" @@ -126,40 +126,23 @@ { "cell_type": "markdown", "metadata": { - "id": "12rSAfnUtPmJ" + "id": "xL5x2AwLpeTH" }, "source": [ - "DeepMIMO provides multiple [scenarios](https://deepmimo.net/scenarios/) that one can select from. Here, we use the `O1_60` scenario, which is an instance of O1 with the carrier frequency set to 60 GHz.\n", + "DeepMIMO provides multiple [scenarios](https://deepmimo.net/scenarios/) that one can select from. In this example, we use the O1 scenario with the carrier frequency set to 60 GHz (O1_60). To run this example, please download the \"O1_60\" data files [from this page](https://deepmimo.net/scenarios/o1-scenario/). The downloaded zip file should be extracted into a folder, and the parameter `DeepMIMO_params['dataset_folder']` should be set to point to this folder, as done below.\n", "\n", - "To use DeepMIMO with Sionna, the DeepMIMO dataset first needs to be generated. The generated DeepMIMO dataset contains channels for different locations of the users and basestations. The layout of the O1 scenario is shown in the figure below.\n", + "To use DeepMIMO with Sionna, the DeepMIMO dataset first needs to be generated. The generated DeepMIMO dataset contains channels for different locations of the users and basestations. The layout of the O1 scenario is shown in the figure below.\n", "\n", - 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+ "![img](https://deepmimo.net/examples/sionna_adapter_example.png)\n", "\n", "In this example, we generate a dataset that consists of channels for the links from the basestation 6 to the users located on the rows 400 to 450. Each of these rows consists of 181 user locations, resulting in $51 \\times 181 = 9231$ basestation-user channels.\n", "\n", - "The antenna arrays in the DeepMIMO dataset are defined as number of horizontal & vertical elements. In the following example, a single-user MISO downlink is considered. The basestation is equipped with a uniform linear array of 16 elements, which is oriented towards the positive x-axis by default. The users are each equipped with a single antenna. These parameters can be configured using the code below (for more information about the DeepMIMO parameters, please check [the DeepMIMO documentation](https://deepmimo.net/documentation)." + "The antenna arrays in the DeepMIMO dataset are defined through the x-y-z axes. In the following example, a single-user MISO downlink is considered. The basestation is equipped with a uniform linear array of 16 elements spread along the x-axis. The users are each equipped with a single antenna. These parameters can be configured using the code below (for more information about the DeepMIMO parameters, please check [the DeepMIMO configurations](https://deepmimo.net/versions/v2-python/))." ] }, { "cell_type": "code", - "source": [ - "# Import DeepMIMO (install package if import fails)\n", - "try:\n", - " import deepmimo as dm\n", - "except ImportError as e:\n", - " import os\n", - " os.system(\"!pip install deepmimo\")\n", - " import deepmimo as dm" - ], - "metadata": { - "id": "Y39zbDbyuQ2J" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-03-13T01:48:45.918949Z", @@ -167,113 +150,84 @@ "iopub.status.idle": "2025-03-13T01:48:52.133244Z", "shell.execute_reply": "2025-03-13T01:48:52.132383Z" }, - "id": "h8cNH5cGtPmN", - "outputId": "edf9507e-66a9-47f8-cd14-e3f977c3f0d0", - "colab": { - "base_uri": "https://localhost:8080/" - } + "id": "VyEyyzfLpeTR", + "outputId": "998331e6-cdfb-4939-8632-2dcca8330372" }, "outputs": [ { - "output_type": "stream", "name": "stdout", - "text": [ - "Downloading scenario 'o1_60'\n" - ] - }, - { "output_type": "stream", - "name": "stderr", "text": [ - "Downloading: 100%|██████████| 2.30G/2.30G [00:45<00:00, 54.6MB/s]\n" + "Collecting DeepMIMO\n", + " Downloading DeepMIMO-1.0-py3-none-any.whl.metadata (421 bytes)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/site-packages (from DeepMIMO) (1.26.4)\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.10/site-packages (from DeepMIMO) (1.15.2)\n", + "Collecting tqdm (from DeepMIMO)\n", + " Downloading tqdm-4.67.1-py3-none-any.whl.metadata (57 kB)\n", + "Downloading DeepMIMO-1.0-py3-none-any.whl (21 kB)\n", + "Downloading tqdm-4.67.1-py3-none-any.whl (78 kB)\n", + "Installing collected packages: tqdm, DeepMIMO\n", + "Successfully installed DeepMIMO-1.0 tqdm-4.67.1\n" ] }, { - "output_type": "stream", "name": "stdout", - "text": [ - "✓ Downloaded to /content/deepmimo_scenarios/o1_60_downloaded.zip\n" - ] - }, - { "output_type": "stream", - "name": "stderr", "text": [ - "Extracting: 100%|██████████| 4161/4161 [02:37<00:00, 26.34file/s] \n" + "\n", + "Basestation 6\n", + "\n", + "UE-BS Channels\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "✓ Unzipped and moved to /content/deepmimo_scenarios\n", - "✓ Scenario 'o1_60' ready to use!\n", - "Loading TXRX PAIR: TXset 6 (tx_idx 0) & RXset 0 (rx_idxs 497931)\n" + "\n", + "BS-BS Channels\n" ] } ], "source": [ - "# Download the dataset\n", - "dm.download('O1_60')\n", - "\n", - "# Load a BS-RX grid combination\n", - "load_params = {'max_paths': 10, 'tx_sets': [6], 'rx_sets': [0]}\n", - "dataset = dm.load('O1_60', **load_params)\n", + "# Import DeepMIMO\n", + "try:\n", + " import DeepMIMO\n", + "except ImportError as e:\n", + " # Install DeepMIMO if package is not already installed\n", + " import os\n", + " os.system(\"pip install DeepMIMO\")\n", + " import DeepMIMO\n", "\n", - "# Select a subset of users in the dataset (and trim matrices)\n", - "sel_usr_idxs = dataset.get_row_idxs(range(400, 451))\n", - "dataset_t = dataset.subset(sel_usr_idxs)" - ] - }, - { - "cell_type": "code", - "source": [ "# Channel generation\n", - "params = dm.ChannelParameters() # Load the default parameters\n", + "DeepMIMO_params = DeepMIMO.default_params() # Load the default parameters\n", + "DeepMIMO_params['dataset_folder'] = r'./scenarios' # Path to the downloaded scenarios\n", + "DeepMIMO_params['scenario'] = 'O1_60' # DeepMIMO scenario\n", + "DeepMIMO_params['num_paths'] = 10 # Maximum number of paths\n", + "DeepMIMO_params['active_BS'] = np.array([6]) # Basestation indices to be included in the dataset\n", + "\n", + "# Selected rows of users, whose channels are to be generated.\n", + "DeepMIMO_params['user_row_first'] = 400 # First user row to be included in the dataset\n", + "DeepMIMO_params['user_row_last'] = 450 # Last user row to be included in the dataset\n", "\n", "# Configuration of the antenna arrays\n", - "params.bs_antenna.shape = [16, 1] # BS antenna shape [horizontal, vertical]\n", - "params.ue_antenna.shape = [1, 1] # UE antenna shape [horizontal, vertical]\n", + "DeepMIMO_params['bs_antenna']['shape'] = np.array([16, 1, 1]) # BS antenna shape through [x, y, z] axes\n", + "DeepMIMO_params['ue_antenna']['shape'] = np.array([1, 1, 1]) # UE antenna shape through [x, y, z] axes\n", "\n", - "# Configure time domain channels. Sionna will generate in frequency domain\n", - "params.freq_domain = False\n", + "# The OFDM_channels parameter allows choosing between the generation of channel impulse\n", + "# responses (if set to 0) or frequency domain channels (if set to 1).\n", + "# It is set to 0 for this simulation, as the channel responses in frequency domain\n", + "# will be generated using Sionna.\n", + "DeepMIMO_params['OFDM_channels'] = 0\n", "\n", "# Generates a DeepMIMO dataset\n", - "dataset_t.compute_channels(params)\n", - "dataset_t.channels.shape # (num_rx, num_rx_ant, num_tx_ant, num_time)" - ], - "metadata": { - "id": "XkPe8gvwuPfk", - "outputId": "fe0fe508-7a82-4639-d46c-86c5b0be6967", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Generating channels: 100%|██████████| 9231/9231 [00:00<00:00, 29525.95it/s]\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(9231, 1, 16, 10)" - ] - }, - "metadata": {}, - "execution_count": 5 - } + "DeepMIMO_dataset = DeepMIMO.generate_data(DeepMIMO_params)" ] }, { "cell_type": "markdown", "metadata": { - "id": "dhOdsh0rtPmN" + "id": "DSqUbtFJpeTU" }, "source": [ "### Visualization of the dataset\n", @@ -283,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-03-13T01:48:52.136681Z", @@ -291,50 +245,84 @@ "iopub.status.idle": "2025-03-13T01:48:52.372533Z", "shell.execute_reply": "2025-03-13T01:48:52.371889Z" }, - "id": "0ShXyVmItPmN", - "outputId": "0b9b8a8c-4076-408e-9027-cf0160c3a797", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 388 - } + "id": "4IIUY0aqpeTV", + "outputId": "8729363f-2a48-4ad8-e3bd-3f474d30bc1f" }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": 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", 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XLmDcuHEoLi7G6NGjy1wvgwoiIiIVRKh/l4dYMU1x2b59+7Bw4UKMHTtWkf7+++9j+/bt2LhxI9q2bYs33nhDVVDh0VtK586dC0EQFFPLli0BANeuXcOECRPQokUL1KlTB40aNcLEiRORl5enqMN2eUEQsG7dOk9sDhER1QKWngq1U1Xy3XffoXfv3nbpvXr1wnfffQcAeOihh/Drr7+qqtfjPRUJCQnYsWOH9NnLy9SkrKwsZGVl4dVXX0V8fDwuXryIsWPHIisrC1988YWijtWrV6Nfv37S5+Dg4EppOxER1T414fJH/fr1sWXLFkyePFmRvmXLFtSvXx8A8Ndff6l+AJbHgwovLy9ERETYpbdu3RobN26UPsfGxmLRokUYMmQIiouLpeADMAURjuogIiJyt5oQVKSmpmLcuHHYtWuXNKbiyJEj2LZtG9577z0AQHp6Onr06KGqXo8/UfPs2bOIiopC06ZNkZycjEuXLjktm5eXB51OpwgoACAlJQUNGjRAx44dsWrVKohiyVevioqKoNfrFRMREVFtMXr0aOzevRsBAQH48ssv8eWXX8Lf3x+7d+/GqFGjAAAvvvgiPv/8c1X1erSnolOnTvjoo4/QokULZGdnY968ebj//vtx8uRJuy6XP//8EwsWLMBzzz2nSJ8/fz569uwJf39/bN++HePHj0d+fj4mTpzodL1paWmYN29ehWwTERHVbDWhpwIAunXrhm7durm1TkEs7Wd9Jbpx4wZiYmKwbNkyKVICAL1ej7///e+oX78+vvrqK6ePGAVMt8msXr0amZmZTssUFRWhqKhIUX90dLTUE0JERNWTXq9HUFBQhXyfW+ru9p/n4RXgq2rZ4r+K8N9H3qpS55nz589j9erV+PXXX7F8+XKEhYXhm2++QaNGjZCQkOBSnR6//CEXHByM5s2b49y5c1LazZs30a9fPwQGBmLTpk0lBhSAqffj8uXLiqDBlq+vL3Q6nWIiIiIqi+r6QjG53bt3o02bNjh06BA2btyI/Px8AMD//vc/zJkzx+V6q1RQkZ+fj/PnzyMyMhKAKSrs06cPfHx88NVXX8HPz6/UOjIyMlCvXj34+qqLIomIiMqiJtxSOmPGDCxcuBDp6enw8fGR0nv27ImDBw+6XK9Hx1RMnToVAwYMQExMDLKysjBnzhxotVokJSVJAUVBQQE+++wzxYDK0NBQaLVabNmyBVeuXEHnzp3h5+eH9PR0LF68GFOnTvXkZhERUQ0migJElUGC2vIV7cSJE1i7dq1delhYGP7880+X6/VoUHH58mUkJSXh6tWrCA0NxX333YeDBw8iNDQUP/zwAw4dOgQAiIuLUyx34cIFNG7cGN7e3nj77bcxefJkiKKIuLg4LFu2TNXTv4iIiGqb4OBgZGdno0mTJor048eP46677nK5Xo8GFSU9+fKBBx4o9dbQfv36KR56RUREVNFqwt0fTz/9NKZPn44NGzZAEAQYjUb897//xdSpUzFs2DCX661SYyqIiIiqOsvlD7VTVbJ48WK0bNkS0dHRyM/PR3x8PLp3746uXbti1qxZLtfr8SdqEhERVSeiCz0VVS2o8PHxwYoVK5CamoqTJ08iPz8f7du3R7NmzcpVL4MKIiIiFUQAap/wVGUeCGWjUaNGaNSokdvqY1BBRESkghECBJXPnVD7nAqDwYC5c+fis88+Q05ODqKiovDss89i1qxZEAT7usaOHYv3338fr7/+OiZNmuSwzilTppR5/cuWLVPVXgsGFURERFXMyy+/jHfffRcff/wxEhIS8OOPP2LEiBEICgqyew3Fpk2bcPDgQURFRZVY5/HjxxWfjx07huLiYrRo0QIAcObMGWi1Wtxzzz0ut5tBBRERkQqV8ZyK/fv345FHHkFiYiIAoHHjxvj3v/+Nw4cPK8r9/vvvmDBhAr777juprDO7du2S5pctW4bAwEB8/PHHqFevHgDg+vXrGDFiBO6//35VbZXj3R9EREQqlOeJmrZvyHb2SomuXbti586dOHPmDADT47P37duH/v37W9thNGLo0KGYNm2a6nd1vPbaa0hLS5MCCgCoV68eFi5ciNdee03tLpGwp4KIiEgFUXRhoKa5fHR0tCJ9zpw5mDt3rl35GTNmQK/Xo2XLltBqtTAYDFi0aBGSk5OlMi+//DK8vLxKfCu3M3q9Hn/88Ydd+h9//IGbN2+qrs+CQQUREZEK5bn8kZmZqXiJpbP3VK1fvx5r1qzB2rVrkZCQgIyMDEyaNAlRUVEYPnw4jh49in/96184duyYw4GbpXnssccwYsQIvPbaa+jYsSMA4NChQ5g2bRoGDRqkuj4LBhVEREQqlCeoKOubsadNm4YZM2bg6aefBgC0adMGFy9eRFpaGoYPH469e/ciNzdXcTuowWDAiy++iOXLl+O3334rsf733nsPU6dOxTPPPIM7d+4AALy8vDBq1Ci88sorqrZNjkEFERFRFVNQUACNRjnsUavVwmg0AgCGDh2K3r17K/L79u2LoUOHYsSIEaXW7+/vj3feeQevvPIKzp8/DwCIjY1FQEBAudrNoIKIiEgFoyhAqOB3fwwYMACLFi1Co0aNkJCQgOPHj2PZsmUYOXIkACAkJAQhISGKZby9vRERESHdIloWAQEBaNu2raq2lYR3fxAREalgGaipdlLjzTffxOOPP47x48ejVatWmDp1KsaMGYMFCxa43O6xY8fi8uXLZSr7+eefY82aNarXwZ4KIiIiFUxBgtoxFerWERgYiOXLl2P58uVlXqa0cRShoaFISEhAt27dMGDAAHTo0AFRUVHw8/PD9evXcerUKezbtw/r1q1DVFQUPvjgA3WNBoMKIiIiVSrj4VcVYcGCBXj++efx4Ycf4p133sGpU6cU+YGBgejduzc++OAD9OvXz6V1MKggIiJSQYT6F4RVlReKhYeH46WXXsJLL72E69ev49KlS7h16xYaNGiA2NhYl25PlWNQQUREVAvVq1dP8URNd2BQQUREpEJ1vfxRGRhUEBERqVGdr39UMAYVREREarjQUwH2VBAREZGt8rxQrKZjUEFERKQCx1Q4x6CCiIioFmjfvn2Zbxk9duyYS+tgUEFERKSGKKgfI1EFeioeffTRCl8HgwoiIiIVquuYijlz5lT4Ojz6QrG5c+dCEATF1LJlSym/sLAQKSkpCAkJQd26dTF48GBcuXJFUcelS5eQmJgIf39/hIWFYdq0aSguLq7sTSEiotpCdHGqBTzeU5GQkIAdO3ZIn728rE2aPHkyvv76a2zYsAFBQUF4/vnnMWjQIPz3v/8FABgMBiQmJiIiIgL79+9HdnY2hg0bBm9vbyxevLjSt4WIiGq+mjBQ02Aw4PXXX8f69etx6dIl3L59W5F/7do1l+r1+KvPvby8EBERIU0NGjQAAOTl5WHlypVYtmwZevbsiXvuuQerV6/G/v37cfDgQQDA9u3bcerUKXz22Wdo164d+vfvjwULFuDtt9+220FERERuU817KebNm4dly5bhqaeeQl5eHqZMmYJBgwZBo9Fg7ty5Ltfr8aDi7NmziIqKQtOmTZGcnIxLly4BAI4ePYo7d+6gd+/eUtmWLVuiUaNGOHDgAADgwIEDaNOmDcLDw6Uyffv2hV6vx88//+x0nUVFRdDr9YqJiIiotlizZg1WrFiBF198EV5eXkhKSsKHH36I2bNnSz/cXeHRoKJTp0746KOP8O233+Ldd9/FhQsXcP/99+PmzZvIycmBj48PgoODFcuEh4cjJycHAJCTk6MIKCz5ljxn0tLSEBQUJE3R0dHu3TAiIqqxLJc/1E5VSU5ODtq0aQMAqFu3LvLy8gAADz/8ML7++muX6/VoUNG/f3888cQTaNu2Lfr27Ytt27bhxo0bWL9+fYWud+bMmcjLy5OmzMzMCl0fERHVIDVgoGbDhg2RnZ0NAIiNjcX27dsBAEeOHIGvr6/L9Xr88odccHAwmjdvjnPnziEiIgK3b9/GjRs3FGWuXLmCiIgIAEBERITd3SCWz5Yyjvj6+kKn0ykmIiKishFcnKqOxx57DDt37gQATJgwAampqWjWrBmGDRuGkSNHulyvx+/+kMvPz8f58+cxdOhQ3HPPPfD29sbOnTsxePBgAMDp06dx6dIldOnSBQDQpUsXLFq0CLm5uQgLCwMApKenQ6fTIT4+3mPbQURENVgNeEvpkiVLpPmnnnoKMTEx2L9/P5o1a4YBAwa4XK9Hg4qpU6diwIABiImJQVZWFubMmQOtVoukpCQEBQVh1KhRmDJlCurXrw+dTocJEyagS5cu6Ny5MwCgT58+iI+Px9ChQ7F06VLk5ORg1qxZSElJKVf3DRERkVM1IKgoLCyEn5+f9Llz587SubU8PBpUXL58GUlJSbh69SpCQ0Nx33334eDBgwgNDQUAvP7669BoNBg8eDCKiorQt29fvPPOO9LyWq0WW7duxbhx49ClSxcEBARg+PDhmD9/vqc2iYiIqMoLCwvDY489hiFDhqBXr17QaNwzGkIQxarw8FDP0uv1CAoKQl5eHsdXEBFVYxX5fW6pO/rtedDU8St9ARnjrUJkpsypMueZTZs2Ye3atfj6668RFBSEp556CkOGDEGHDh3KVW+VGqhJRERU1Vne/aF2qkoee+wxbNiwAVeuXMHixYtx6tQpdO7cGc2bNy9Xb7+qnoobN25g06ZN2Lt3Ly5evIiCggKEhoaiffv26Nu3L7p27epyQzyJPRVERDVDZfRUNHzTtZ6KyxOqTk+FI6dOnUJycjJ++uknGAwGl+ooU09FVlYW/vGPfyAyMhILFy7ErVu30K5dO/Tq1QsNGzbErl278Pe//x3x8fH4/PPPXWoIERFRtWB59bnaqQoqLCzE+vXr8eijj+Luu+/GtWvXMG3aNJfrK9NAzfbt22P48OE4evSo01s1b926hc2bN2P58uXIzMzE1KlTXW4UERFRVSWIpkntMlXJd999h7Vr12Lz5s3w8vLC448/ju3bt6N79+7lqrdMQcWpU6cQEhJSYpk6deogKSlJupuDiIiIqqbHHnsMDz/8MD755BM89NBD8Pb2dku9ZQoqSgsoylueiIio2qgBz6m4cuUKAgMD3V6vS8+pyMrKwr59+5Cbmwuj0ajImzhxolsaRkREVCW5MkaiCoyp0Ov10iBRURRLfEO3q4NJVQcVH330EcaMGQMfHx+EhIRAEKw7ShAEBhVERFSzVdOeinr16iE7OxthYWEIDg5WnL8tRFGEIAgu3/2hOqhITU3F7NmzMXPmTLc9gYuIiKjaqKZBxffff4/69esDAHbt2lUh61AdVBQUFODpp59mQEFERFSN9OjRAwBQXFyM3bt3Y+TIkWjYsKFb16E6Mhg1ahQ2bNjg1kYQERFVG6KLUxXh5eWFV155BcXFxe6vW+0CaWlpePjhh/Htt9+iTZs2drehLFu2zG2NIyIiqnKq6UBNuZ49e2L37t1o3LixW+t1Kaj47rvv0KJFCwCwG6hJRERUk9WEh1/1798fM2bMwIkTJ3DPPfcgICBAkT9w4ECX6lUdVLz22mtYtWoVnn32WZdWSEREVK1V04GacuPHjwfg+OpCpd794evri27durm0MiIiIvI822dMuYvqgZovvPAC3nzzzYpoCxEREVWwO3fuwMvLCydPnnR73ap7Kg4fPozvv/8eW7duRUJCgt1AzS+//NJtjSMiIqpqBLgwpqJCWuIab29vNGrUyOVLHCVRHVQEBwdj0KBBbm8IERFRtVAD7v546aWX8H//93/49NNPpQdiuYPqoGL16tVuWzkREVG1UwMGar711ls4d+4coqKiEBMTY3f3x7Fjx1yq16UXihEREdVaNSCoePTRRyuk3jIFFf369cPcuXPRuXPnEsvdvHkT77zzDurWrYuUlBS3NJCIiKgqqQnPqZgzZ47TvPKMtShTUPHEE09g8ODBCAoKwoABA9ChQwdERUXBz88P169fx6lTp7Bv3z5s27YNiYmJeOWVV1xuEBEREVW+M2fOYOXKlfjkk0+QnZ3tUh1lCipGjRqFIUOGYMOGDfj888/xwQcfIC8vD4DpIRnx8fHo27cvjhw5glatWrnUECIiomqhBlz+sCgoKMDnn3+OVatW4cCBA+jQoQOmTJnicn1lHlPh6+uLIUOGYMiQIQCAvLw83Lp1CyEhIXa3lRIREdVYNSCoOHjwID788ENs2LABjRo1wi+//IJdu3bh/vvvL1e9Lr+/PCgoCBEREW4LKJYsWQJBEDBp0iQAwG+//QZBEBxO8rekOspft26dW9pERERkyzKmQu1UFbz22mtISEjA448/jnr16mHPnj04ceIEBEFASEhIueuvEnd/HDlyBO+//z7atm0rpUVHR9td0/nggw/wyiuvoH///or01atXo1+/ftLn4ODgCm0vERHVYtX4ORXTp0/H9OnTMX/+fGi1WrfX73JPhbvk5+cjOTkZK1asQL169aR0rVaLiIgIxbRp0yY8+eSTqFu3rqKO4OBgRTk/P7/K3gwiIqotRBenKmDBggXYsGEDmjRpgunTp7v9Ud0eDypSUlKQmJiI3r17l1ju6NGjyMjIwKhRoxzW0aBBA3Ts2BGrVq2CKFaRvx4REVEVMnPmTJw5cwaffvopcnJy0KlTJ/ztb3+DKIq4fv16uev3aFCxbt06HDt2DGlpaaWWXblyJVq1aoWuXbsq0ufPn4/169cjPT0dgwcPxvjx40t94VlRURH0er1iIiIiKovKGFNhMBiQmpqKJk2aoE6dOoiNjcWCBQukH8137tzB9OnT0aZNGwQEBCAqKgrDhg1DVlZWmerv0aMHPv74Y+Tk5GD8+PG455570KNHD3Tt2tXh69DLSnVQMXz4cOzZs8flFVpkZmbihRdewJo1a0q9XHHr1i2sXbvWYS9FamoqunXrhvbt22P69On45z//WepzMtLS0hAUFCRN0dHR5doWIiKqRSrh8sfLL7+Md999F2+99RZ++eUXvPzyy1i6dKn0o7mgoADHjh1Damoqjh07hi+//BKnT5/GwIEDVa0nMDAQY8aMwaFDh3D8+HF07NgRS5YsUddYGUFUea3g0UcfxbZt2xATE4MRI0Zg+PDhuOuuu1SvePPmzXjssccUA0UMBgMEQYBGo0FRUZGU9+mnn2LUqFH4/fffERoaWmK9X3/9NR5++GEUFhbC19fXYZmioiIUFRVJn/V6PaKjo5GXlwedTqd6W4iIqGrQ6/UICgqqkO9zS91NUxdDq3LsnqGwEL8u+L8yt+vhhx9GeHg4Vq5cKaUNHjwYderUwWeffeZwmSNHjqBjx464ePEiGjVqpKp9cnfu3HH5zk7VPRWbN2/G77//jnHjxuHzzz9H48aN0b9/f3zxxRe4c+dOmevp1asXTpw4gYyMDGnq0KEDkpOTkZGRoQg2Vq5ciYEDB5YaUABARkYG6tWr5zSgAEzP3NDpdIqJiIioTMrRU2F76V3+A1eua9eu2LlzJ86cOQMA+N///od9+/bZ3f0ol5eXB0EQyn0HZHkeFeHSLaWhoaGYMmUKpkyZgmPHjmH16tUYOnQo6tatiyFDhmD8+PFo1qxZiXUEBgaidevWirSAgACEhIQo0s+dO4c9e/Zg27ZtdnVs2bIFV65cQefOneHn54f09HQsXrwYU6dOdWWziIiISleOh1/ZXm6fM2cO5s6da1d8xowZ0Ov1aNmyJbRaLQwGAxYtWoTk5GSH1RcWFmL69OlISkry6A/lcj2nIjs7G+np6UhPT4dWq8VDDz2EEydOID4+HkuXLsXkyZPL3cBVq1ahYcOG6NOnj12et7c33n77bUyePBmiKCIuLg7Lli3D6NGjy71eIiIid8vMzFSc9J31qq9fvx5r1qzB2rVrkZCQgIyMDEyaNAlRUVEYPny4ouydO3fw5JNPQhRFvPvuuxXa/tKoHlNx584dfPXVV1i9ejW2b9+Otm3b4h//+AeeeeYZaUdt2rQJI0eOdMvtKZWhIq/BERFR5amMMRWx/+famIrzi8s+piI6OhozZsxQvPF74cKF+Oyzz/D//t//k9IsAcWvv/6K77//vsSnYk6ZMgULFixAQEAA9uzZg65du8LLy73PwFRdW2RkJIxGI5KSknD48GG0a9fOrsyDDz7Ip1oSERG5qKCgABqNctijVquF0WiUPlsCirNnz2LXrl2lPmb7zTffxPTp0xEQEIAHH3wQ2dnZCAsLc2u7VQcVr7/+Op544okSbwMNDg7GhQsXytUwIiKiKqkSXig2YMAALFq0CI0aNUJCQgKOHz+OZcuWYeTIkQBMAcXjjz+OY8eOYevWrTAYDMjJyQEA1K9fHz4+PnZ1Nm7cGG+88Qb69OkDURRx4MABxZOs5bp3766uwWaqL3/URLz8QURUM1TG5Y+4Ga5d/ji3pOyXP27evInU1FRs2rQJubm5iIqKQlJSEmbPng0fHx/89ttvaNKkicNld+3ahQceeMAuffPmzRg7dixyc3MhCILTp08LggCDwaBq+6RlGVQwqCAiqikqLajwVRlUFKkLKipSfn4+dDodTp8+7fTyR1BQkEt1V4m3lBIREVHlqFu3Lnbt2oUmTZp4fqAmERFRrVYJYyoqWo8ePWAwGLBx40b88ssvAID4+Hg88sgj5XolOoMKIiIiFVx5QZja8hXt3LlzSExMxOXLl9GiRQsApvdiRUdH4+uvv0ZsbKxL9Xr81edERETVSiW8UKyiTZw4EU2bNkVmZiaOHTuGY8eO4dKlS2jSpAkmTpzocr3sqSAiIlKhJvRU7N69GwcPHkT9+vWltJCQECxZsgTdunVzuV4GFURERGrUgDEVvr6+uHnzpl16fn6+w2dclBUvfxAREdUyDz/8MJ577jkcOnQIoihCFEUcPHgQY8eOxcCBA12ul0EFERGRGjVgTMUbb7yB2NhYdOnSBX5+fvDz80O3bt0QFxeHf/3rXy7Xy8sfREREKtSEMRXBwcH4z3/+g3Pnzkm3lLZq1QpxcXHlqpdBBRERkRo1YEyFRVxcXLkDCTkGFURERGrUoKDC3RhUEBERqVATLn9UFA7UJCIiIrdgTwUREZEavPzhFHsqiIiIVLBc/lA7VTV79+7FkCFD0KVLF/z+++8AgE8//RT79u1zuU4GFURERGrUgOdUbNy4EX379kWdOnVw/PhxFBUVAQDy8vKwePFil+tlUEFERKRGDQgqFi5ciPfeew8rVqyAt7e3lN6tWzccO3bM5Xo5poKIiEgFwTypXaYqOX36NLp3726XHhQUhBs3brhcL3sqiIiIapmIiAicO3fOLn3fvn1o2rSpy/UyqCAiIlKjBlz+GD16NF544QUcOnQIgiAgKysLa9aswdSpUzFu3DiX6+XlDyIiIhVqwsOvZsyYAaPRiF69eqGgoADdu3eHr68vpk6digkTJrhcb5XpqViyZAkEQcCkSZOktAceeACCICimsWPHKpa7dOkSEhMT4e/vj7CwMEybNg3FxcWV3HoiIqo1akBPhSAIeOmll3Dt2jWcPHkSBw8exB9//IEFCxaUq94q0VNx5MgRvP/++2jbtq1d3ujRozF//nzps7+/vzRvMBiQmJiIiIgI7N+/H9nZ2Rg2bBi8vb3LdUsMERFRiapYkOAqHx8fxMfHu60+jwcV+fn5SE5OxooVK7Bw4UK7fH9/f0RERDhcdvv27Th16hR27NiB8PBwtGvXDgsWLMD06dMxd+5c+Pj4VHTziYiolqmulz8GDRpU5rJffvmlS+vw+OWPlJQUJCYmonfv3g7z16xZgwYNGqB169aYOXMmCgoKpLwDBw6gTZs2CA8Pl9L69u0LvV6Pn3/+ucLbTkREVF0EBQWVeXKVR3sq1q1bh2PHjuHIkSMO85955hnExMQgKioKP/30E6ZPn47Tp09LEVROTo4ioAAgfc7JyXG63qKiIunpYQCg1+vLuylERFRbVNN3f6xevbrC1+GxoCIzMxMvvPAC0tPT4efn57DMc889J823adMGkZGR6NWrF86fP4/Y2FiX152WloZ58+a5vDwREdVe1fXyR2XwWFBx9OhR5Obm4u6775bSDAYD9uzZg7feegtFRUXQarWKZTp16gQAOHfuHGJjYxEREYHDhw8ryly5cgUAnI7DAICZM2diypQp0me9Xo/o6OhybxMREdUC1bSnQq59+/YQBPvnfAqCAD8/P8TFxeHZZ5/Fgw8+qKpej42p6NWrF06cOIGMjAxp6tChA5KTk5GRkWEXUABARkYGACAyMhIA0KVLF5w4cQK5ublSmfT0dOh0uhJHs/r6+kKn0ykmIiKisqgJbynt168ffv31VwQEBODBBx/Egw8+iLp16+L8+fO49957kZ2djd69e+M///mPqno91lMRGBiI1q1bK9ICAgIQEhKC1q1b4/z581i7di0eeughhISE4KeffsLkyZPRvXt36dbTPn36ID4+HkOHDsXSpUuRk5ODWbNmISUlBb6+vp7YLCIiqulqQE/Fn3/+iRdffBGpqamK9IULF+LixYvYvn075syZgwULFuCRRx4pc70ev/vDGR8fH+zYsQN9+vRBy5Yt8eKLL2Lw4MHYsmWLVEar1WLr1q3QarXo0qULhgwZgmHDhimea0FERERK69evR1JSkl36008/jfXr1wMAkpKScPr0aVX1evw5FXI//PCDNB8dHY3du3eXukxMTAy2bdtWga0iIiKSqQE9FX5+fti/fz/i4uIU6fv375dunjAajU5vpHCmSgUVREREVV1NuPtjwoQJGDt2LI4ePYp7770XgOnp1h9++CH+7//+DwDw3XffoV27dqrqZVBBRESkRg3oqZg1axaaNGmCt956C59++ikAoEWLFlixYgWeeeYZAMDYsWNVv7GUQQUREZEKgihCENVFCWrLV4bk5GQkJyc7za9Tp47qOhlUEBERqVEDeiosbt++jdzcXBiNRkV6o0aNXKqPQQUREVEtc/bsWYwcORL79+9XpIuiCEEQYDAYXKqXQQUREZEKNWGg5rPPPgsvLy9s3boVkZGRDp+u6QoGFURERGrUgMsfGRkZOHr0KFq2bOnWehlUEBERqVATeiri4+Px559/ur3eKvtETSIioipJdHGqQl5++WX885//xA8//ICrV69Cr9crJlexp4KIiEiFmtBT0bt3bwCml3vKcaAmERERqbJr164KqZdBBRERkRo1YKBmjx49nOadPHnS5Xo5poKIiEglyyWQsk5V3c2bN/HBBx+gY8eO+Nvf/uZyPQwqiIiI1BBF16YqaM+ePRg+fDgiIyPx6quvomfPnjh48KDL9fHyBxERkQrVfaBmTk4OPvroI6xcuRJ6vR5PPvkkioqKsHnzZsTHx5erbvZUEBER1RIDBgxAixYt8NNPP2H58uXIysrCm2++6bb62VNBRESkRjUeqPnNN99g4sSJGDduHJo1a+b2+tlTQUREpIJgdG2qCvbt24ebN2/innvuQadOnfDWW2+59cmaDCqIiIjUqIQnahoMBqSmpqJJkyaoU6cOYmNjsWDBAoiyAZ+iKGL27NmIjIxEnTp10Lt3b5w9e7bEejt37owVK1YgOzsbY8aMwbp16xAVFQWj0Yj09HTcvHlTXUNtMKggIiJSQe3tpK4M7Hz55Zfx7rvv4q233sIvv/yCl19+GUuXLlWMf1i6dCneeOMNvPfeezh06BACAgLQt29fFBYWllp/QEAARo4ciX379uHEiRN48cUXsWTJEoSFhWHgwIFqd4mEQQUREZEalXBL6f79+/HII48gMTERjRs3xuOPP44+ffrg8OHD5iaIWL58OWbNmoVHHnkEbdu2xSeffIKsrCxs3rxZ1bpatGiBpUuX4vLly/j3v/+tallbDCqIiIgqie2Lu4qKihyW69q1K3bu3IkzZ84AAP73v/9h37596N+/PwDgwoULyMnJkd7hAQBBQUHo1KkTDhw44FLbtFotHn30UXz11VcuLQ/w7g8iIiJVyvOciujoaEX6nDlzMHfuXLvyM2bMgF6vR8uWLaHVamEwGLBo0SIkJycDMD1rAgDCw8MVy4WHh0t5nsCggoiISI1y3FKamZkJnU4nJfv6+josvn79eqxZswZr165FQkICMjIyMGnSJERFRWH48OGutbsSMKggIiJSoTw9FTqdThFUODNt2jTMmDEDTz/9NACgTZs2uHjxItLS0jB8+HBEREQAAK5cuYLIyEhpuStXrqBdu3bqGudGVWZMxZIlSyAIAiZNmgQAuHbtGiZMmIAWLVqgTp06aNSoESZOnIi8vDzFcoIg2E3r1q3zwBYQEVGtUAkDNQsKCqDRKE/RWq0WRqPpgRdNmjRBREQEdu7cKeXr9XocOnQIXbp0Kf82uqhK9FQcOXIE77//Ptq2bSulZWVlISsrC6+++iri4+Nx8eJFjB07FllZWfjiiy8Uy69evRr9+vWTPgcHB1dW04mIqJapjHd/DBgwAIsWLUKjRo2QkJCA48ePY9myZRg5cqSpPvOP8IULF6JZs2Zo0qQJUlNTERUVhUcffVTdytzI40FFfn4+kpOTsWLFCixcuFBKb926NTZu3Ch9jo2NxaJFizBkyBAUFxfDy8va9ODgYKkriIiIqLp78803kZqaivHjxyM3NxdRUVEYM2YMZs+eLZX55z//ib/++gvPPfccbty4gfvuuw/ffvst/Pz8PNZuj1/+SElJQWJiouK2GGfy8vKg0+kUAYWljgYNGqBjx45YtWqV4oljREREblUJT9QMDAzE8uXLcfHiRdy6dQvnz5/HwoUL4ePjI5URBAHz589HTk4OCgsLsWPHDjRv3rz821cOHu2pWLduHY4dO4YjR46UWvbPP//EggUL8NxzzynS58+fj549e8Lf3x/bt2/H+PHjkZ+fj4kTJzqtq6ioSHFvsF6vd30jiIioVqnurz6vSB4LKjIzM/HCCy8gPT291K4avV6PxMRExMfH293Pm5qaKs23b98ef/31F1555ZUSg4q0tDTMmzevXO0nIqJayiiaJrXL1AIeu/xx9OhR5Obm4u6774aXlxe8vLywe/duvPHGG/Dy8oLBYAAA3Lx5E/369UNgYCA2bdoEb2/vEuvt1KkTLl++7PQpZQAwc+ZM5OXlSVNmZqZbt42IiGqwSrj8UV15rKeiV69eOHHihCJtxIgRaNmyJaZPnw6tVgu9Xo++ffvC19cXX331VZkGn2RkZKBevXpOHygCmB42UlI+ERGRMwJcuPxRIS2pejwWVAQGBqJ169aKtICAAISEhKB169bQ6/Xo06cPCgoK8Nlnn0nPSQeA0NBQaLVabNmyBVeuXEHnzp3h5+eH9PR0LF68GFOnTvXEJhEREdVqHr+l1Jljx47h0KFDAIC4uDhF3oULF9C4cWN4e3vj7bffxuTJkyGKIuLi4rBs2TKMHj3aE00mIqLawIWHWakuX01VqaDihx9+kOYfeOCBUm8N7devn+KhV0RERBWNd384V6WCCiIioiqvHC8Uq+kYVBAREakgiCIElZcz1JavrhhUEBERqWE0T2qXqQU8/phuIiIiqhnYU0FERKQCL384x6CCiIhIDQ7UdIpBBRERkRp8ToVTDCqIiIhU4HMqnGNQQUREpAZ7Kpzi3R9ERETkFuypICIiUkEwmia1y9QGDCqIiIjU4OUPpxhUEBERqcFbSp1iUEFERKQCH37lHIMKIiIiNXj5wyne/UFERERuwZ4KIiIiNUSof+to7eioYFBBRESkBsdUOMeggoiISA0RLoypqJCWVDkMKoiIiNTgQE2nGFQQERGpYQQguLBMLcC7P4iIiMgt2FNBRESkAgdqOseggoiISA2OqXCKQQUREZEaDCqcqjJjKpYsWQJBEDBp0iQprbCwECkpKQgJCUHdunUxePBgXLlyRbHcpUuXkJiYCH9/f4SFhWHatGkoLi6u5NYTEVGtYQkq1E61QJUIKo4cOYL3338fbdu2VaRPnjwZW7ZswYYNG7B7925kZWVh0KBBUr7BYEBiYiJu376N/fv34+OPP8ZHH32E2bNnV/YmEBFRbWF0caoFPB5U5OfnIzk5GStWrEC9evWk9Ly8PKxcuRLLli1Dz549cc8992D16tXYv38/Dh48CADYvn07Tp06hc8++wzt2rVD//79sWDBArz99tu4ffu2pzaJiIioVvJ4UJGSkoLExET07t1bkX706FHcuXNHkd6yZUs0atQIBw4cAAAcOHAAbdq0QXh4uFSmb9++0Ov1+Pnnn52us6ioCHq9XjERERGVheXuD7VTbeDRgZrr1q3DsWPHcOTIEbu8nJwc+Pj4IDg4WJEeHh6OnJwcqYw8oLDkW/KcSUtLw7x588rZeiIiqpU4UNMpj/VUZGZm4oUXXsCaNWvg5+dXqeueOXMm8vLypCkzM7NS109ERNWYUXRtqgU8FlQcPXoUubm5uPvuu+Hl5QUvLy/s3r0bb7zxBry8vBAeHo7bt2/jxo0biuWuXLmCiIgIAEBERITd3SCWz5Yyjvj6+kKn0ykmIiKiMuHdH055LKjo1asXTpw4gYyMDGnq0KEDkpOTpXlvb2/s3LlTWub06dO4dOkSunTpAgDo0qULTpw4gdzcXKlMeno6dDod4uPjK32biIioNnAloKgdQYXHxlQEBgaidevWirSAgACEhIRI6aNGjcKUKVNQv3596HQ6TJgwAV26dEHnzp0BAH369EF8fDyGDh2KpUuXIicnB7NmzUJKSgp8fX0rfZuIiIhqsyr9RM3XX38dGo0GgwcPRlFREfr27Yt33nlHytdqtdi6dSvGjRuHLl26ICAgAMOHD8f8+fM92GoiIqrROFDTKUEUa8mWlkCv1yMoKAh5eXkcX0FEVI1V5Pe5pe7eMc/DS6OuN7zYWIQdF9+q8eeZKt1TQUREVOWIRtOkdplagEEFERGRGrz84RSDCiIiIjWMLtzNwedUEBEREZUdeyqIiIjU4OUPpxhUEBERqSHChaCiQlpS5fDyBxERkRqV8Jjuxo0bQxAEuyklJQWA6aWZQ4cORUREBAICAnD33Xdj48aNFbG1qrCngoiISA2jEYDKW0SN6sofOXIEBoNB+nzy5En8/e9/xxNPPAEAGDZsGG7cuIGvvvoKDRo0wNq1a/Hkk0/ixx9/RPv27dW1zY3YU0FERKRGJfRUhIaGIiIiQpq2bt2K2NhY9OjRAwCwf/9+TJgwAR07dkTTpk0xa9YsBAcH4+jRoxWxxWXGoIKIiKgKu337Nj777DOMHDkSgiAAALp27YrPP/8c165dg9FoxLp161BYWIgHHnjAo23l5Y8yuHw2W5q/8tsfiGgcCggCrlz8A+Exoab0S38ivFEDwPwHN6VdRXijENN85lVTvmU+OgSQigq4cvkawhvWN+Vfvobw6PqQFcCV368j/K56gCCbB3Al6wbCo+pZGytY0oIBADnZNxARFQzRXFfD6Pq4nHlNKpuTk4eIiCBTWcu8dbXIuZKHiPAgxbwoCMjJzUNEWJBiP2Xn5iEyzLS8NC/P/8O6TPYfeYgMNc1n/ZmHqAbK9Wb9qUdUA9OjbLOu6hEVogME03xkiPIRt1nX9Iiqb863zMv8fk2Pu8xpv19XzkfVk5UVgN9v6HFXsDlfNi/VdUOPu8zLXM7To2GQcl6UbcPlvDw0DDJt42V9HhrqTNsozctcvpmHhoHmfMu8RFTm5+ehYV1zvbJ5qa6/bqBh3WBpPjogGJZRYo0DQ/Bb/lXr9hTcwF3+wRAAZBVcR5S/9VgSBBFZt64jqo4pLfvWdUTWqQcBQPata4iUlwWQU3gNEX71IQC4UngN4eayFrmFVxHmFwIBwB9FVxHqWx+CAPxR9CdCfRvI6hJxtehPhPg2gADgatEfCPENhSCIuHb7D9T3CVWUvX47F/V9wgAAN25fQT3zvGkbAP3tHOh8IiBAhP5ODnTepvmbd7IR6B0JQTaC7q87WajrHQUBIgruZMHfJ0q2DSIKi39HHa+7AACFxZdRx6shBIgoKr4MX6+GirJ3ijPh4xUNANZ5ASguzoS3Od2y74zFl6HxipbNN4TGqwmoiirH3R96vV6R7OvrW+oLMDdv3owbN27g2WefldLWr1+Pp556CiEhIfDy8oK/vz82bdqEuLg4de1yMwYVpbh8Nhv/SJhkOobMXxmCVgNBo4UoihA0GkCrAURLusZ07Gg1pnyt1pwmQtBqAEEDEablBK25rGD6WhM0AqDRSvOO8kWNeV4QAGleA2gEQABEwZovldWYygIiHuzTGt9/fwqAqaxRBASNaR1GS1mtqaw8XxQgawOU9YoijPJ8wXS1URDMZSDCqIFUv1SXAIgaSOuQylraIgCQ5SvmBUsbLfUCkK23xHybstb1KvNFWb4oioAAGABozPkGmDffvG0ac3tFiFL9tvmiIMrKCuayomJ5I0T7fNG0XkFjnZenawSYjytlWVEUYT1ERTwc3Rrbfj9pKiuYvhg1GkCjMc3LDiUIghEwt0UriNK8RmNN1wimfaSBASJEaAXAS2O0zgumL1KNYEkT4CUYIcJomtcYTPMQTNsMEd6yNC+NAYARGvO8ACM0ggANRAgQ4SWY8wUB3oIBAgzmeUsbjaZlIMBbKJbK+ljmYVoOALSC0bS8uaxlXV6CAaY/jQFeKIYgaKCFCAHFEKCBj3m9gqCBDyzrFeFtzteY948ADbwFmNuugbegMZWFAC8AAjTQCoJp30ML39B0BhZVVTkefhUdHa1InjNnDubOnVvioitXrkT//v0RFRUlpaWmpuLGjRvYsWMHGjRogM2bN+PJJ5/E3r170aZNG3VtcyMGFaXY++VBGIotA2zMJ/JiIwSt6WQuGoymCFSrhWgwQjCK5rOlaAosDAaIRlNAIRqMgGD6xhcNRilgAERTvQbRdPY2z9vmGw2i6QxlOcmJIkSNBqJoNC0nCBA1pvWb3hNnDiyMopT//Y6fYRRNAYNUrxHmswhgNJqXk/LN44sEQcoXYfpsaq+pLvPqTO22BCkiYPmHJ5p3n1GWLwX7lnWYT9yiYD4pizDvD0u+rKxgChiUaZACD2m9DvItuwvmP5NoDj4sZW3zLcEJRMs+suYbLOuS5mXLOcrXyNKNpoBBqteuLpt885eSYJkXTPvBaASMgqlnAUZZPkQIAmAw/80EAdiaeRJGc0AhiNZ8S1nRKJoPFdF08hMAg2gNKAyi6XiXp5v3oJQviLJ5mOoSzWlG0QCDVNYAQTSa5mH+dySIMEhppnytABhhgNEyLxpMdUKE0VzWKBpghEHKN22jEUbRFNyI5uVNMbABRpiXM6cLgijVL9qsS4QRMAdCpmPKAFEwmn9iFEMUjeZ/psUQBVOwA9FoDkyNUkBqmjcfHzCYl7P0k2jMaYLpRwKKYSj4Bhrd+NK/pKjSiaLR9L2rchkAyMzMVLxQrLReiosXL2LHjh348ssvpbTz58/jrbfewsmTJ5GQkAAA+Nvf/oa9e/fi7bffxnvvvaeqbe7EoKIUZ348b5MiyLrpzQGEJYiwzAM2aXCcL6tSkSbK0iCLh23yRdt12BBlZS2zokE0/yQ2p8vX5aAOaV2KfEGRrSjuqB2O6pVXZbO9Jebbli0hX3SSL2+PXdsc1W+TLzrId/qbxbYtihXZlhVLyBeVh55tmZL2say8JeYtiW2+s11vWb88X1NCWcFSWHD0JzMFFJZTrLO6lIerKNVV8npFmKKtkg8lU51Gh2XhYBkBRkUbSipbchtNYZlg6Qm11H77JKiKEkX1j902X/7Q6XSq3lK6evVqhIWFITExUUorKCgAAGg0ymGRWq0WRpV3mbgbg4pSyU/+ppO4oNHIvnmtgYIgDxhk85YvC/tva5uzuW3QoQhQIH0hA5ZfyvJ6BUWaaFlesKlLYz0pipazlADZr37Bmi9vnpRv2wb7E6Z8OVFev1SXYFdOfrKxpgvWumV1WOoE4DDfjk39UppsXr4NdvMyls/WdYnK7YDt/hCVQYyz9tnmK9on2s/L9oHpZGxJs+bLdqNyXZbyinxLL4hol2bpjbAvaypvPvpk7ZYHG9Z5IwCtrD5BkNVrPvmbDllrD4hU1ibYsB5GluXMPTcwmucdr1cKMKR6jYp9bddGqaxtXfJAQZS12bYuxyce20NVXq8RIrRlixLJU0T7v3fZllHHaDRi9erVGD58OLy8rKfrli1bIi4uDmPGjMGrr76KkJAQbN68Genp6di6davq9bgTg4rSmC5OA4D52jtMgYI5zXyR2fylLpgvdkPqDZDGO9jmK070lnybspaTqixNtMkHzAGEJWCVTsTWs4riJCyv1ybNWtZRvnVeETSYz15OgwbbE67t8g7aaN0+ONwGeX2Ot9Fm3mZddu2V2l3yvF0Q5Gh5R+l2+aKyrPyzw3xHddt8QQmy/1iCC0fBiKXHQDqpQzqZS0UtJ2v7Q02ZLqvLGp/KlxOluhT5ikDCdvMsJ3NrWVGq1yjbFmsdUj6MtrG3zTpMZbVS4GINCqwBhLWdomXbpT0j34dGKcyR1ytvv7Q/ofxNYN8ZJjicJ9qxYwcuXbqEkSNHKtK9vb2xbds2zJgxAwMGDEB+fj7i4uLw8ccf46GHHvJQa00YVJRCEARZMKExfTlqtIoTvdRDYenBEExlBUAKOhT5UrrsWxuCddQbLIGCPB+KfOu3nWD+6SY7kZvTRJuzgt2vYOvZwTTOwZxmPdHb5su+YS0nZgdBg5QmG/CobLOD5SEr6ygokbZNvi6b5QG7+qSyiu123AZFr4dtXbBZxtLt7WwdAKRxH/J8eTBguz3mZZT5opQvyOYdBT2CfB7WNEHWk2ENKGSBgk3wYPnlb1mfPFiQBxyKumT5gizfMq9RbIc14JDHjoqONctysnyNPE+aAI25rdZ/BtbeDVN5o1RWkLdBsQtFaV1QrMu6Pg2gWK9iect+ke93eV02kzx80Mg+CZD1bFLVZTSNs1FF5RgMAOjTp495fJy9Zs2aVYknaNpiUFEaAbDrqZCd3AXT0HlzWfO3IARFr4bTSxrSvHV520BAmoe1F8FZWXmvhl3vg13QYBM82J3IZd+Kdm2QpQnySxbWz3YnXCe9F/a9B7KeDCjzHZ1InfY+CDYnedtyim1z8N/S6nK2vDTvPHhwti0l5Qu2+bJ1yE/2Uk+C1Esgr0+UpVvLagRrr4VdDwWsJ3jYzkuHiKPeC/lmyIIT2bosdUvtkgczUl3yyyrmdUGUAgnTgE3rPwspmJDqMn2RW4MT6zgImOc1iuWN1nWax1coeyvkQYJ9j4T8n6jUbPlnQR5OCDbLCzZl5blUpVTS5Y/qiEFFaQSNXU8FBMEUTADKk7/GfFnE5uRvN0l1KS+VSOUtaZaTrCwgUPRkCLCvVyoHu5O/dMKWLm9IZwKbXgBBGRDIB3YKTgIES0AhQPlINcF6icTC4UBHy7evk5Oso3EatnXIWceNWNstBQew3V4HPQ629QpwGuwo1uEkTd67oWy3KFu/PF/5y16RLltesek2+0V+8jZ9tpm32W+OeyKs7VT0LEjbI7tUIT/MzWl2+bIeA0tAoTxBy/OhKGsNNqxtkwcElh4ZeY+Doq0w3bFh23NgvZRitFu38vKIbOyKLN/0X3mAIdrUL69L2S754EyNPA8Cn0xYhYlGI0SVPRVq7xaprhhUlEYQ7E/+issY1nEQduMnbMtKPRkO5uHgkodtXVLPgGB/ycPcRHnvhfzyhTyQkHoEFMGKbHnLvOySh/OxDzZBg8bmhCtvo9QWOE6Tn4xt1yVrg3W9jss6uizjrA3OgpgS67IdLGnTLkXw4KDdtv8tKd/S62DfVpvBmbI/NQTlIaPotQBgO5ZCkOUrxklAdkKXllee3DWWtiiWt7ZXypdtliDbHstnqR3y9snHTgjWQMdal3wgp31AYO1xsPRuWNOt5a2DN63LWwZzyv8UtsGOZRtM6xdl5QBlkCFIqVbKAENQ5IsAB2pWdeypcIpBRWlsT+6CeQyFoqdCNn5CHkzIloFdvnVedJSv6JGwCRQE2bztOpwFGuazjXQJRfp2lg+cVHwjQxFQSN9xtgMtZeuzFlFul+1JWpZmt244yZfVaxtQOKwbzgIC570etmMi5D0W1nlRUc5pOxTnBJsufJtAQApCHLRVIjipx/Ify8nQ0WUX6aOoOGEru+lFKRixHmL2gYV9T4TsRC0/fATrOAr5ydsaOEDxWXZTkfLkbf6vcjyD8k4R66FuvQtDfleKctfKekdkD/SylNEo6rb55wBHAYi87cp1wW552OVb0wTzPEdUUPXGoKIUgqxHQHAy4NJu/ITtSV7xDekg2LDpiQAE6QFR8nzFIEjF8qbPihOcxklPheKWUWu+oqxGsDtZOg4QBOsJVwpg7C9lWMdcWOqylpW2DfJ6YeXgRO3s7g5F+2zqsAswHKzDWeBhXcZRj4FNvu16ofzsMF+RJvsVLwC2vR7ycRSWSxLWgEJ+yCgHZ8p/CwuCfB6yOhxskuXELf35lT0RUmAgX6/gIF9x6UI5ZsK0DtOtm9ajQTYgUmqbaLMO25O/7VgMoyJgkPIF67xGVq+8x8E0WcZXWOu17ZGwXAKybJv80gtkdVnnlSMqlGUFqYzyL0FVjtH6dy8z9lSQicZ+/ISs98I0hkJ28peXldIs86XfMmo9oSuDBsUlD8slDLueCNnJ37ycpV7FIEjbMjbffHbPqZDV5ShosD8JC/YncnndpZzQHQUPaoIGu4AI9tvjsFdDvlxJ9dq2z8l6Tf+1CTQc9UqUsA8cnV+kIEBKs6xDHnAAiiAIssMXNid/WAMHQVGHfHnlOAtLT4Qi2JAfEoLl5C7bJHn9ijSbdsl+8VsDBGd3ephO/hBEmwDCug1SgCFfj2LXKgMQS53ydgGO7+Kw/5PZb59lWdsxFcq7PqhaEZWXyMq+TM3HoKJUsm9v2TMoFM+pkOfb9j5I+ULpt4w6uaQhfzaF/FKJsqws8LDUL5ja7zAIMCcpy9r0PsjSXAoaHKxLGYDY1Cs46NWQ59l889r/ylfWBZu6Sg1Q5MvY1VvSOAn7yxIO22bbLku9Ur58bIR8fU4uadgfPopLFlKadPK3XoKQqjAvY3/YObvjw1lPhHwzRWXdsFlGlqY40cv/yUg9INZLFfY9Bda7OxQDOR08x8L6bArzeApBNg/rNtumyS+3mFZsfe+J7eEj//PIt8dSszJyEGRzppbJ7/bgQM2qzfQ4e3VBgrNbQ2saBhWl0cBmfARg1+NQ4gOtBEh3jZg/Sz0R8jrh4C4PxUlXkE2WNPP/CbLAQDAHF7aBhEb2NSsPIMzFFIMyLWnyOuRpJQYNgsMeAftejxLqldXt9Btb0UabdpRWTn5yt63bpt1OAw2bddn2WigCEWk5214LB9sISDGoXbscPDnTNoZVpNnWafNfuyta0uEi67WQDjknPRE2h7HdOAvzdiuWkf2at5zIrQGP3eElBRS2d3UAon3vg7wdsvVC3g7zJ2vvgfyyiXy/KOuQ/VNStNv2FlPbQ1bZqwKbNlnyBXO+7SUQqpJE8+PcVS9T8zGoKEWAzt/6DSB/BoXDb2ebeQC2AYjDQZnOnmkhDzIAB5c8BNm8LF1alzlN3kMijSoTbL79ZOsQSk5zPD5CflaDdTnLZ0Wa4DSQsBtb4aAux5cZ7Oftytl+2ztsm22eg3ESinUpH8NtrcdB74Jiu2zrVY6JkNWmbKfs5GTtIZEFGLJ2QfYr35Iv77WQLmnIykKeLztBK3eBbX2OxlnYnkAdXfaQBxiWdSjHVjgKQBQ9GIpy1p4GZU+C0b5NNu21D1Dse0WsJxFruzSybYNt3TYs/3zlAYSjMtLzKjSBDushz2NPhXMMKkpxV2y4zfgJKE/+tr0UJTwl0+4pmtZ3TFt7LzSW5WBdzpLv4Imaol1Z0/8p7vIwp4sANBrz20PlJ1Ob51BI65OlKQdympeT6raux+ltojZpFk4HWso5WK60eu3aI1/Gdp1lWJdd+TK20S7QcVivbQDiYNyF7MRuObQEWR02f3775prr0Jhfpy4/hBXNVxxesoGgspOto2BDsF1Gtk7Y1WstJ19Gfsun6VC37bmwv1PEGjhZ862bZQoEZIcs5ANCLXUr6jM/8MpufIZN3Yp9BcU/C+X+lH22ZcoXZJ/N/7NU7NXYwVJEVRuDClgjSL1eb5f3tweawbCo2PzJfFayPLjKcobSADAA0Fo6VEVI1xtEmwc3mG60M32DWn4ACRrpmqria9py8jb/9hEMyjs6pHRRdulElAcgliaa00Sg+30t8f3e06YMrTnd/CpvU1Pkl0eUaaZ6rZtm+80qv2xi2U3O6pKWMdclyE64ouzbWVEXlMsp1it/JLjsG15aXr4uOLgcYlnOYV2iVJej9dveZmptl6g8Y2msZQVzvVLbzCdWU70iIJiutJu23XSgiBAgPUFSA1geviMIgFHjqNdC1qNgzu8dFY/tOSfNAabppCsKljdZWE/mpu0xnYC1ljaZT7YwL2eq2LJvbPMtYxlMbdRqTCdrIwQYYQAgwktjSrHMi9Kvf1NZa77pleaWdWgFQAtL20xltdDAW7AGBBpzr4IlzbqMAV4CcAcAUAwvAF6WNgoiAAO8BeCOuawGGvgIBtlhYYBWEHBbgHm9li9RI7wE68vIvMz7RQNAKwgARHhDkN4PooFGWs6S7wXB3AYRGggQfLpCcPCdRCWzfI9XZM9AsVgEtZczis1HXU0niLWlT6YEly9fRnR0tKebQUREbpKZmYmGDRu6tc7CwkI0adIEOTk5Li0fERGBCxcuwM/Pz63tqkoYVMD0etmsrCwEBgaitj9vX6/XIzo6GpmZmdDpdJ5uTrXCfec67jvXcd8piaKImzdvIioqChqNpvQFVCosLMTt27ddWtbHx6dGBxQAL38AADQajdsj2upOp9PxC8pF3Heu475zHfedVVBQUIXV7efnV+MDg/LgrdBERETkFgwqiIiIyC0YVJCCr68v5syZA19fX083pdrhvnMd953ruO+oKuFATSIiInIL9lQQERGRWzCoICIiIrdgUEFERERuwaCCiIiI3IJBRS2QlpaGe++9F4GBgQgLC8Ojjz6K06dPK8o88MADEARBMY0dO1ZR5tKlS0hMTIS/vz/CwsIwbdo0FBcXoyYry74DgAMHDqBnz54ICAiATqdD9+7dcevWLSn/2rVrSE5Ohk6nQ3BwMEaNGoX8/PzK3JRKV9q+++233+yOOcu0YcMGqRyPO8fHXU5ODoYOHYqIiAgEBATg7rvvxsaNGxVlauNxR57FoKIW2L17N1JSUnDw4EGkp6fjzp076NOnD/766y9FudGjRyM7O1uali5dKuUZDAYkJibi9u3b2L9/Pz7++GN89NFHmD17dmVvTqUqy747cOAA+vXrhz59+uDw4cM4cuQInn/+ecUjgpOTk/Hzzz8jPT0dW7duxZ49e/Dcc895YpMqTWn7Ljo6WnG8ZWdnY968eahbty769+8PgMddScfdsGHDcPr0aXz11Vc4ceIEBg0ahCeffBLHjx+XytTG4448TKRaJzc3VwQg7t69W0rr0aOH+MILLzhdZtu2baJGoxFzcnKktHfffVfU6XRiUVFRRTa3SnG07zp16iTOmjXL6TKnTp0SAYhHjhyR0r755htREATx999/r9D2ViWO9p2tdu3aiSNHjpQ+87gzcbTvAgICxE8++URRrn79+uKKFStEUeRxR57BnopaKC8vDwBQv359RfqaNWvQoEEDtG7dGjNnzkRBQYGUd+DAAbRp0wbh4eFSWt++faHX6/Hzzz9XTsOrANt9l5ubi0OHDiEsLAxdu3ZFeHg4evTogX379knLHDhwAMHBwejQoYOU1rt3b2g0Ghw6dKhyN8CDnB13FkePHkVGRgZGjRolpfG4M3G077p27YrPP/8c165dg9FoxLp161BYWIgHHngAAI878gy+UKyWMRqNmDRpErp164bWrVtL6c888wxiYmIQFRWFn376CdOnT8fp06fx5ZdfAjBdv5V/sQOQPrv6GuDqxtG++/XXXwEAc+fOxauvvop27drhk08+Qa9evXDy5Ek0a9YMOTk5CAsLU9Tl5eWF+vXr1+p9Z2vlypVo1aoVunbtKqXxuHO+79avX4+nnnoKISEh8PLygr+/PzZt2oS4uDgA4HFHHsGgopZJSUnByZMnFb+kASius7Zp0waRkZHo1asXzp8/j9jY2MpuZpXkaN8ZjUYAwJgxYzBixAgAQPv27bFz506sWrUKaWlpHmlrVePsuLO4desW1q5di9TU1EpuWdXnbN+lpqbixo0b2LFjBxo0aIDNmzfjySefxN69e9GmTRsPtZZqOwYVtcjzzz8vDdYq7VXvnTp1AgCcO3cOsbGxiIiIwOHDhxVlrly5AgCIiIiomAZXIc72XWRkJAAgPj5eUb5Vq1a4dOkSANP+yc3NVeQXFxfj2rVrtXrfyX3xxRcoKCjAsGHDFOk87hzvu/Pnz+Ott97CyZMnkZCQAAD429/+hr179+Ltt9/Ge++9V+uPO/IMjqmoBURRxPPPP49Nmzbh+++/R5MmTUpdJiMjA4D1pNmlSxecOHFC8SWVnp4OnU5nd0KtSUrbd40bN0ZUVJTd7X5nzpxBTEwMANO+u3HjBo4ePSrlf//99zAajVLwVhOpOe5WrlyJgQMHIjQ0VJHO487xvrOMd5LfYQQAWq1W6j2rrccdeZhnx4lSZRg3bpwYFBQk/vDDD2J2drY0FRQUiKIoiufOnRPnz58v/vjjj+KFCxfE//znP2LTpk3F7t27S3UUFxeLrVu3Fvv06SNmZGSI3377rRgaGirOnDnTU5tVKUrbd6Ioiq+//rqo0+nEDRs2iGfPnhVnzZol+vn5iefOnZPK9OvXT2zfvr146NAhcd++fWKzZs3EpKQkT2xSpSnLvhNFUTx79qwoCIL4zTff2NXB487xvrt9+7YYFxcn3n///eKhQ4fEc+fOia+++qooCIL49ddfS/XUxuOOPItBRS0AwOG0evVqURRF8dKlS2L37t3F+vXri76+vmJcXJw4bdo0MS8vT1HPb7/9Jvbv31+sU6eO2KBBA/HFF18U79y544Etqjyl7TuLtLQ0sWHDhqK/v7/YpUsXce/evYr8q1eviklJSWLdunVFnU4njhgxQrx582YlbknlK+u+mzlzphgdHS0aDAaH9fC4c7zvzpw5Iw4aNEgMCwsT/f39xbZt29rdYlobjzvyLL76nIiIiNyCYyqIiIjILRhUEBERkVswqCAiIiK3YFBBREREbsGggoiIiNyCQQURERG5BYMKIiIicgsGFUREROQWDCqIarCVK1eiT58+5arjzz//RFhYGC5fvuymVhFRTcUnahLVUIWFhWjatCk2bNiAbt26lauuqVOn4vr161i5cqWbWkdENRF7KohqqC+++AI6na7cAQUAjBgxAmvWrMG1a9fc0DIiqqkYVBBVcX/88QciIiKwePFiKW3//v3w8fHBzp07nS63bt06DBgwQJH27LPP4tFHH8XixYsRHh6O4OBgzJ8/H8XFxZg2bRrq16+Phg0bYvXq1YrlEhISEBUVhU2bNrl344ioRmFQQVTFhYaGYtWqVZg7dy5+/PFH3Lx5E0OHDsXzzz+PXr16OV1u37596NChg136999/j6ysLOzZswfLli3DnDlz8PDDD6NevXo4dOgQxo4dizFjxtiNoejYsSP27t3r9u0jopqDYyqIqomUlBTs2LEDHTp0wIkTJ3DkyBH4+vo6LHvjxg3Uq1cPe/bswf333y+lP/vss/jhhx/w66+/QqMx/aZo2bIlwsLCsGfPHgCAwWBAUFAQPvzwQzz99NPSslOmTMHx48exa9euCtxKIqrOvDzdACIqm1dffRWtW7fGhg0bcPToUacBBQDcunULAODn52eXl5CQIAUUABAeHo7WrVtLn7VaLUJCQpCbm6tYrk6dOigoKCjvZhBRDcbLH0TVxPnz55GVlQWj0YjffvutxLIhISEQBAHXr1+3y/P29lZ8FgTBYZrRaFSkXbt2DaGhoa41nohqBQYVRNXA7du3MWTIEDz11FNYsGAB/vGPf9j1JMj5+PggPj4ep06dclsbTp48ifbt27utPiKqeRhUEFUDL730EvLy8vDGG29g+vTpaN68OUaOHFniMn379sW+ffvcsv6CggIcPXq03A/SIqKajUEFURX3ww8/YPny5fj000+h0+mg0Wjw6aefYu/evXj33XedLjdq1Chs27YNeXl55W7Df/7zHzRq1Egx6JOIyBbv/iCqwZ544gncfffdmDlzZrnq6dy5MyZOnIhnnnnGTS0jopqIPRVENdgrr7yCunXrlquOP//8E4MGDUJSUpKbWkVENRV7KoiIiMgt2FNBREREbsGggoiIiNyCQQURERG5BYMKIiIicgsGFUREROQWDCqIiIjILRhUEBERkVswqCAiIiK3YFBBREREbvH/Ac9PQu4sqQDHAAAAAElFTkSuQmCC\n" + "
" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ - " # Plot the azimuth of the AoA\n", - "dataset_t.aoa_az.plot(cbar_title='Angle of Arrival (deg)')" + "plt.figure(figsize=(12,8))\n", + "\n", + "## User locations\n", + "active_bs_idx = 0 # Select the first active basestation in the dataset\n", + "plt.scatter(DeepMIMO_dataset[active_bs_idx]['user']['location'][:, 1], # y-axis location of the users\n", + " DeepMIMO_dataset[active_bs_idx]['user']['location'][:, 0], # x-axis location of the users\n", + " s=1, marker='x', c='C0', label='The users located on the rows %i to %i (R%i to R%i)'%\n", + " (DeepMIMO_params['user_row_first'], DeepMIMO_params['user_row_last'],\n", + " DeepMIMO_params['user_row_first'], DeepMIMO_params['user_row_last']))\n", + "# First 181 users correspond to the first row\n", + "plt.scatter(DeepMIMO_dataset[active_bs_idx]['user']['location'][0:181, 1],\n", + " DeepMIMO_dataset[active_bs_idx]['user']['location'][0:181, 0],\n", + " s=1, marker='x', c='C1', label='First row of users (R%i)'% (DeepMIMO_params['user_row_first']))\n", + "\n", + "## Basestation location\n", + "plt.scatter(DeepMIMO_dataset[active_bs_idx]['location'][1],\n", + " DeepMIMO_dataset[active_bs_idx]['location'][0],\n", + " s=50.0, marker='o', c='C2', label='Basestation')\n", + "\n", + "plt.gca().invert_xaxis() # Invert the x-axis to align the figure with the figure above\n", + "plt.ylabel('x-axis')\n", + "plt.xlabel('y-axis')\n", + "plt.grid()\n", + "plt.legend();" ] }, { "cell_type": "markdown", "metadata": { - "id": "YlwUGc-NtPmO" + "id": "DAFp6Ly6peTW" }, "source": [ "## Using DeepMIMO with Sionna\n", "\n", "The DeepMIMO Python package provides [a Sionna-compliant channel impulse response generator](https://nvlabs.github.io/sionna/phy/tutorials/CIR_Dataset.html#Generators) that adapts the structure of the DeepMIMO dataset to be consistent with Sionna.\n", "\n", - "An adapter is instantiated for a given DeepMIMO dataset. The adapter only requires the dataset to adapt and it will generate samples for all users and basestations in that dataset.\n", + "An adapter is instantiated for a given DeepMIMO dataset. In addition to the dataset, the adapter takes the indices of the basestations and users, to generate the channels between these basestations and users:\n", + "\n", + "`DeepMIMOSionnaAdapter(DeepMIMO_dataset, bs_idx, ue_idx)`\n", + "\n", + "\n", + "**Note:** `bs_idx` and `ue_idx` set the links from which the channels are drawn. For instance, if `bs_idx = [0, 1]` and `ue_idx = [2, 3]`, the adapter then outputs the 4 channels formed by the combination of the first and second basestations with the third and fourth users.\n", + "\n", + "The default behavior for `bs_idx` and `ue_idx` are defined as follows:\n", + "- If value for `bs_idx` is not given, it will be set to `[0]` (i.e., the first basestation in the `DeepMIMO_dataset`).\n", + "- If value for `ue_idx` is not given, then channels are provided for the links between the `bs_idx` and all users (i.e., `ue_idx=range(len(DeepMIMO_dataset[0]['user']['channel']))`.\n", + "- If the both `bs_idx` and `ue_idx` are not given, the channels between the first basestation and all the users are provided by the adapter. For this example, `DeepMIMOSionnaAdapter(DeepMIMO_dataset)` returns the channels from the basestation 6 and the 9231 available user locations.\n", + "\n", + "**Note:** The adapter assumes basestations are transmitters and users are receivers. Uplink channels can be obtained using (transpose) reciprocity.\n", + "\n", + "### Random Sampling of Multi-User Channels\n", + "\n", + "When considering multiple basestations, `bs_idx` can be set to a 2D numpy matrix of shape $($ # of samples $\\times$ # of basestations per sample $)$. In this case, for each sample of basestations, the `DeepMIMOSionnaAdapter` returns a set of $($ # of basestations per sample $\\times$ # of users $)$ channels, which can be provided as a multi-transmitter sample for the Sionna model. For example, `bs_idx = np.array([[0, 1], [2, 3], [4, 5]])` provides three sets of $($ 2 basestations $\\times$ # of users $)$ channels. These three channel sets are from the basestation sets `[0, 1]`, `[2, 3]`, and `[4, 5]`, respectively, to the users.\n", "\n", - "In this example, `SionnaAdapter(dataset)` will return the channels from the basestation 6 and the 9231 available user locations.\n", + "To use the adapter for multi-user channels, `ue_idx` can be set to a 2D numpy matrix of shape $($ # of samples $\\times$ # of users per sample $)$. In this case, for each sample of users, the `DeepMIMOSionnaAdapter` returns a set of $($ # of basestations $\\times$ # of users per sample $)$ channels, which can be provided as a multi-receiver sample for the Sionna model. For example, `ue_idx = np.array([[0, 1 ,2], [4, 5, 6]])` provides two sets of $($ # of basestations $\\times$ 3 users $)$ channels. These two channel sets are from the basestations to the user sets `[0, 1, 2]` and `[4, 5, 6]`, respectively.\n", "\n", - "**Note:** The adapter assumes basestations are transmitters and users are receivers. Uplink channels can be obtained using (transpose) reciprocity." + "In order to randomly sample channels from all the available user locations considering `num_rx` users, one may set `ue_idx` as in the following cell. In this example, the channels will be randomly chosen from the links between the basestation 6 and the 9231 available user locations." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-03-13T01:48:52.376377Z", @@ -342,19 +330,32 @@ "iopub.status.idle": "2025-03-13T01:48:52.380757Z", "shell.execute_reply": "2025-03-13T01:48:52.380192Z" }, - "id": "6LDSxX9PtPmO" + "id": "-cNtN1ITpeTW" }, "outputs": [], "source": [ - "from deepmimo.integrations import SionnaAdapter\n", - "\n", - "deepmimo_sionna_adapter = SionnaAdapter(dataset_t)" + "from DeepMIMO import DeepMIMOSionnaAdapter\n", + "\n", + "# Number of receivers for the Sionna model.\n", + "# MISO is considered here.\n", + "num_rx = 1\n", + "\n", + "# The number of UE locations in the generated DeepMIMO dataset\n", + "num_ue_locations = len(DeepMIMO_dataset[0]['user']['channel']) # 9231\n", + "# Pick the largest possible number of user locations that is a multiple of ``num_rx``\n", + "ue_idx = np.arange(num_rx*(num_ue_locations//num_rx))\n", + "# Optionally shuffle the dataset to not select only users that are near each others\n", + "np.random.shuffle(ue_idx)\n", + "# Reshape to fit the requested number of users\n", + "ue_idx = np.reshape(ue_idx, [-1, num_rx]) # In the shape of (floor(9231/num_rx) x num_rx)\n", + "\n", + "DeepMIMO_Sionna_adapter = DeepMIMOSionnaAdapter(DeepMIMO_dataset, ue_idx=ue_idx)" ] }, { "cell_type": "markdown", "metadata": { - "id": "rl8wZ1O-tPmO" + "id": "3h8ftZ3KpeTX" }, "source": [ "## Link-level Simulations using Sionna and DeepMIMO\n", @@ -366,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-03-13T01:48:52.384454Z", @@ -374,7 +375,7 @@ "iopub.status.idle": "2025-03-13T01:48:52.397002Z", "shell.execute_reply": "2025-03-13T01:48:52.396376Z" }, - "id": "EeoY1rP9tPmO" + "id": "qFeVqsxtpeTY" }, "outputs": [], "source": [ @@ -483,7 +484,7 @@ { "cell_type": "markdown", "metadata": { - "id": "15OdNJoLtPmO" + "id": "qMYozII0peTZ" }, "source": [ "We next evaluate the setup with different $E_b/N_0$ values to obtain BLER curves." @@ -491,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-03-13T01:48:52.400127Z", @@ -499,29 +500,26 @@ "iopub.status.idle": "2025-03-13T01:49:23.855650Z", "shell.execute_reply": "2025-03-13T01:49:23.854765Z" }, - "id": "WC1tSAq9tPmP", - "outputId": "26a07782-c588-4a32-c706-364f58b3dfe6", - "colab": { - "base_uri": "https://localhost:8080/" - } + "id": "yqe8sR0TpeTZ", + "outputId": "f066ecfd-a522-4b83-9ade-4c85919c38a6" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", "---------------------------------------------------------------------------------------------------------------------------------------\n", - " -7.0 | 1.2781e-01 | 1.0000e+00 | 29840 | 233472 | 128 | 128 | 7.4 |reached target block errors\n", - " -6.806 | 9.7352e-02 | 1.0000e+00 | 22729 | 233472 | 128 | 128 | 1.0 |reached target block errors\n", - " -6.611 | 6.6376e-02 | 9.4531e-01 | 15497 | 233472 | 121 | 128 | 1.0 |reached target block errors\n", - " -6.417 | 3.0492e-02 | 7.9688e-01 | 7119 | 233472 | 102 | 128 | 1.0 |reached target block errors\n", - " -6.222 | 1.1498e-02 | 4.7656e-01 | 5369 | 466944 | 122 | 256 | 2.1 |reached target block errors\n", - " -6.028 | 1.4813e-03 | 1.3281e-01 | 2075 | 1400832 | 102 | 768 | 6.8 |reached target block errors\n", - " -5.833 | 1.6177e-04 | 2.2645e-02 | 1303 | 8054784 | 100 | 4416 | 38.1 |reached target block errors\n", - " -5.639 | 1.4563e-05 | 3.9062e-03 | 170 | 11673600 | 25 | 6400 | 56.0 |reached max iterations\n", - " -5.444 | 3.5979e-06 | 6.2500e-04 | 42 | 11673600 | 4 | 6400 | 54.6 |reached max iterations\n", - " -5.25 | 0.0000e+00 | 0.0000e+00 | 0 | 11673600 | 0 | 6400 | 57.5 |reached max iterations\n", + " -7.0 | 1.2337e-01 | 1.0000e+00 | 28803 | 233472 | 128 | 128 | 7.4 |reached target block errors\n", + " -6.806 | 9.1728e-02 | 9.9219e-01 | 21416 | 233472 | 127 | 128 | 0.1 |reached target block errors\n", + " -6.611 | 6.0393e-02 | 9.3750e-01 | 14100 | 233472 | 120 | 128 | 0.1 |reached target block errors\n", + " -6.417 | 2.7489e-02 | 7.6562e-01 | 9627 | 350208 | 147 | 192 | 0.1 |reached target block errors\n", + " -6.222 | 6.6111e-03 | 3.9844e-01 | 3087 | 466944 | 102 | 256 | 0.2 |reached target block errors\n", + " -6.028 | 9.2416e-04 | 9.9265e-02 | 1834 | 1984512 | 108 | 1088 | 0.9 |reached target block errors\n", + " -5.833 | 7.8896e-05 | 1.3594e-02 | 921 | 11673600 | 87 | 6400 | 5.1 |reached max iterations\n", + " -5.639 | 9.8513e-06 | 2.0313e-03 | 115 | 11673600 | 13 | 6400 | 5.2 |reached max iterations\n", + " -5.444 | 4.2832e-07 | 1.5625e-04 | 5 | 11673600 | 1 | 6400 | 5.2 |reached max iterations\n", + " -5.25 | 0.0000e+00 | 0.0000e+00 | 0 | 11673600 | 0 | 6400 | 5.1 |reached max iterations\n", "\n", "Simulation stopped as no error occurred @ EbNo = -5.2 dB.\n", "\n" @@ -529,16 +527,16 @@ } ], "source": [ - "sim_params = {\"ebno_db\": np.linspace(-7, -5.25, 10),\n", + "sim_params = {\n", + " \"ebno_db\": np.linspace(-7, -5.25, 10),\n", " \"cyclic_prefix_length\" : 0,\n", - " \"pilot_ofdm_symbol_indices\" : [2, 11]}\n", - "\n", + " \"pilot_ofdm_symbol_indices\" : [2, 11],\n", + " }\n", "batch_size = 64\n", - "model = LinkModel(DeepMIMO_Sionna_adapter=deepmimo_sionna_adapter,\n", - " carrier_frequency=dataset_t.rt_params.frequency,\n", + "model = LinkModel(DeepMIMO_Sionna_adapter=DeepMIMO_Sionna_adapter,\n", + " carrier_frequency=DeepMIMO_params['scenario_params']['carrier_freq'],\n", " cyclic_prefix_length=sim_params[\"cyclic_prefix_length\"],\n", " pilot_ofdm_symbol_indices=sim_params[\"pilot_ofdm_symbol_indices\"])\n", - "\n", "ber, bler = sim_ber(model,\n", " sim_params[\"ebno_db\"],\n", " batch_size=batch_size,\n", @@ -549,7 +547,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-03-13T01:49:23.859850Z", @@ -557,33 +555,29 @@ "iopub.status.idle": "2025-03-13T01:49:24.255402Z", "shell.execute_reply": "2025-03-13T01:49:24.254518Z" }, - "id": "q5G8_EU6tPmP", - "outputId": "8c3bffcc-d47d-4cde-9c65-0f6c984ea3cb", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 716 - } + "id": "7fEATWHppeTa", + "outputId": "27758c89-a19d-4d13-f14b-7c465d26dce3" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ - "[]" + "[]" ] }, + "execution_count": 8, "metadata": {}, - "execution_count": 11 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", 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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -597,7 +591,7 @@ { "cell_type": "markdown", "metadata": { - "id": "MY82CFF9tPmP" + "id": "2FwD9U4TpeTb" }, "source": [ "## DeepMIMO License and Citation\n", From ab7ada3c2a04530a474fa6aa24cc06b75c55ed33 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Morais?= <19170303+jmoraispk@users.noreply.github.com> Date: Sun, 29 Jun 2025 22:48:03 -0400 Subject: [PATCH 3/7] add `--pre` to deepmimo install command --- tutorials/phy/DeepMIMO.ipynb | 48 ++++++++++++++++++------------------ 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/tutorials/phy/DeepMIMO.ipynb b/tutorials/phy/DeepMIMO.ipynb index f3de4242a..a3519093b 100644 --- a/tutorials/phy/DeepMIMO.ipynb +++ b/tutorials/phy/DeepMIMO.ipynb @@ -3,7 +3,7 @@ { "cell_type": "markdown", "metadata": { - "id": "tp7_gHACpeS4" + "id": "xbl2LfNzutih" }, "source": [ "# Using the DeepMIMO Dataset with Sionna\n", @@ -14,7 +14,7 @@ { "cell_type": "markdown", "metadata": { - "id": "SpgUmMabpeS-" + "id": "2kLRRfIfutii" }, "source": [ "[DeepMIMO](https://deepmimo.net/) is a generic dataset that enables a wide range of machine/deep learning applications for MIMO systems. It takes as input a set of parameters (such as antenna array configurations and time-domain/OFDM parameters) and generates MIMO channel realizations, corresponding locations, angles of arrival/departure, etc., based on these parameters and on a ray-tracing scenario selected [from those available in DeepMIMO](https://deepmimo.net/scenarios/).\n", @@ -30,7 +30,7 @@ { "cell_type": "markdown", "metadata": { - "id": "70aKlvi6peTA" + "id": "-yMoEzSnutii" }, "source": [ "## GPU Configuration and Imports" @@ -46,7 +46,7 @@ "iopub.status.idle": "2025-03-13T01:48:45.899584Z", "shell.execute_reply": "2025-03-13T01:48:45.898771Z" }, - "id": "p-UvC0UUpeTC" + "id": "LcvoCE4Tutij" }, "outputs": [], "source": [ @@ -94,7 +94,7 @@ "iopub.status.idle": "2025-03-13T01:48:45.914833Z", "shell.execute_reply": "2025-03-13T01:48:45.913951Z" }, - "id": "_lwRzt-mpeTE" + "id": "oS5pgNWqutij" }, "outputs": [], "source": [ @@ -117,7 +117,7 @@ { "cell_type": "markdown", "metadata": { - "id": "StLgR-2PpeTF" + "id": "NrN65-MZutij" }, "source": [ "## Configuration of DeepMIMO" @@ -126,14 +126,14 @@ { "cell_type": "markdown", "metadata": { - "id": "xL5x2AwLpeTH" + "id": "NUDT2tKRutij" }, "source": [ "DeepMIMO provides multiple [scenarios](https://deepmimo.net/scenarios/) that one can select from. In this example, we use the O1 scenario with the carrier frequency set to 60 GHz (O1_60). To run this example, please download the \"O1_60\" data files [from this page](https://deepmimo.net/scenarios/o1-scenario/). The downloaded zip file should be extracted into a folder, and the parameter `DeepMIMO_params['dataset_folder']` should be set to point to this folder, as done below.\n", "\n", "To use DeepMIMO with Sionna, the DeepMIMO dataset first needs to be generated. The generated DeepMIMO dataset contains channels for different locations of the users and basestations. The layout of the O1 scenario is shown in the figure below.\n", "\n", - "![img](https://deepmimo.net/examples/sionna_adapter_example.png)\n", + 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"\n", "In this example, we generate a dataset that consists of channels for the links from the basestation 6 to the users located on the rows 400 to 450. Each of these rows consists of 181 user locations, resulting in $51 \\times 181 = 9231$ basestation-user channels.\n", "\n", @@ -150,8 +150,8 @@ "iopub.status.idle": "2025-03-13T01:48:52.133244Z", "shell.execute_reply": "2025-03-13T01:48:52.132383Z" }, - "id": "VyEyyzfLpeTR", - "outputId": "998331e6-cdfb-4939-8632-2dcca8330372" + "id": "jtbZswoSutim", + "outputId": "b300a422-9bd1-4f4a-954a-3431211515b9" }, "outputs": [ { @@ -196,7 +196,7 @@ "except ImportError as e:\n", " # Install DeepMIMO if package is not already installed\n", " import os\n", - " os.system(\"pip install DeepMIMO\")\n", + " os.system(\"uv pip install --pre deepmimo\")\n", " import DeepMIMO\n", "\n", "# Channel generation\n", @@ -227,7 +227,7 @@ { "cell_type": "markdown", "metadata": { - "id": "DSqUbtFJpeTU" + "id": "x1G0ERliutim" }, "source": [ "### Visualization of the dataset\n", @@ -245,8 +245,8 @@ "iopub.status.idle": "2025-03-13T01:48:52.372533Z", "shell.execute_reply": "2025-03-13T01:48:52.371889Z" }, - "id": "4IIUY0aqpeTV", - "outputId": "8729363f-2a48-4ad8-e3bd-3f474d30bc1f" + "id": "qtqlhXIxutin", + "outputId": "3fdcce80-0cc8-415d-fb9c-9a17e1092cb8" }, "outputs": [ { @@ -290,7 +290,7 @@ { "cell_type": "markdown", "metadata": { - "id": "DAFp6Ly6peTW" + "id": "BnlfwY2futin" }, "source": [ "## Using DeepMIMO with Sionna\n", @@ -330,7 +330,7 @@ "iopub.status.idle": "2025-03-13T01:48:52.380757Z", "shell.execute_reply": "2025-03-13T01:48:52.380192Z" }, - "id": "-cNtN1ITpeTW" + "id": "6d1ZcRNhutin" }, "outputs": [], "source": [ @@ -355,7 +355,7 @@ { "cell_type": "markdown", "metadata": { - "id": "3h8ftZ3KpeTX" + "id": "BhfzqX3qutin" }, "source": [ "## Link-level Simulations using Sionna and DeepMIMO\n", @@ -375,7 +375,7 @@ "iopub.status.idle": "2025-03-13T01:48:52.397002Z", "shell.execute_reply": "2025-03-13T01:48:52.396376Z" }, - "id": "qFeVqsxtpeTY" + "id": "q_RJxXKEutin" }, "outputs": [], "source": [ @@ -484,7 +484,7 @@ { "cell_type": "markdown", "metadata": { - "id": "qMYozII0peTZ" + "id": "rMHYPBW4utin" }, "source": [ "We next evaluate the setup with different $E_b/N_0$ values to obtain BLER curves." @@ -500,8 +500,8 @@ "iopub.status.idle": "2025-03-13T01:49:23.855650Z", "shell.execute_reply": "2025-03-13T01:49:23.854765Z" }, - "id": "yqe8sR0TpeTZ", - "outputId": "f066ecfd-a522-4b83-9ade-4c85919c38a6" + "id": "q6tUA3qNutin", + "outputId": "698c42f5-9fc2-4ac2-8ea0-b57ac253d8d6" }, "outputs": [ { @@ -555,8 +555,8 @@ "iopub.status.idle": "2025-03-13T01:49:24.255402Z", "shell.execute_reply": "2025-03-13T01:49:24.254518Z" }, - "id": "7fEATWHppeTa", - "outputId": "27758c89-a19d-4d13-f14b-7c465d26dce3" + "id": "f91WTHfAutin", + "outputId": "4a7a1bd8-0c3b-489a-bb3f-64de186a12f7" }, "outputs": [ { @@ -591,7 +591,7 @@ { "cell_type": "markdown", "metadata": { - "id": "2FwD9U4TpeTb" + "id": "qfue1Q_7utio" }, "source": [ "## DeepMIMO License and Citation\n", From ebcd077f7a9b11be09c17e45446e27b1b5f008ad Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Morais?= <19170303+jmoraispk@users.noreply.github.com> Date: Wed, 2 Jul 2025 09:39:18 -0400 Subject: [PATCH 4/7] fix last commit - ready for MR --- DeepMIMOv4_tutorial_sionna.ipynb | 635 +++++++++++++++++++++++++++++++ 1 file changed, 635 insertions(+) create mode 100644 DeepMIMOv4_tutorial_sionna.ipynb diff --git a/DeepMIMOv4_tutorial_sionna.ipynb b/DeepMIMOv4_tutorial_sionna.ipynb new file mode 100644 index 000000000..c35001783 --- /dev/null +++ b/DeepMIMOv4_tutorial_sionna.ipynb @@ -0,0 +1,635 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "lSbcn0aLtPmH" + }, + "source": [ + "# Using the DeepMIMO Dataset with Sionna\n", + "\n", + "In this example, you will learn how to use the ray-tracing based DeepMIMO dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Thv9PInhtPmI" + }, + "source": [ + "[DeepMIMO](https://deepmimo.net/) is a generic dataset that enables a wide range of machine/deep learning applications for MIMO systems. It takes as input a set of parameters (such as antenna array configurations and time-domain/OFDM parameters) and generates MIMO channel realizations, corresponding locations, angles of arrival/departure, etc., based on these parameters and on a ray-tracing scenario selected [from those available in DeepMIMO](https://deepmimo.net/scenarios/).\n", + "\n", + "## Table of Contents\n", + "* [GPU Configuration and Imports](#GPU-Configuration-and-Imports)\n", + "* [Configuration of DeepMIMO](#Configuration-of-DeepMIMO)\n", + "* [Using DeepMIMO with Sionna](#Using-DeepMIMO-with-Sionna)\n", + "* [Link-level Simulations using Sionna and DeepMIMO](#Link-level-Simulations-using-Sionna-and-DeepMIMO)\n", + "* [DeepMIMO License and Citation](#DeepMIMO-License-and-Citation)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G-qy_3RatPmI" + }, + "source": [ + "## GPU Configuration and Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:42.830858Z", + "iopub.status.busy": "2025-03-13T01:48:42.830249Z", + "iopub.status.idle": "2025-03-13T01:48:45.899584Z", + "shell.execute_reply": "2025-03-13T01:48:45.898771Z" + }, + "id": "yuejsob6tPmJ" + }, + "outputs": [], + "source": [ + "import os\n", + "if os.getenv(\"CUDA_VISIBLE_DEVICES\") is None:\n", + " gpu_num = 0 # Use \"\" to use the CPU\n", + " os.environ[\"CUDA_VISIBLE_DEVICES\"] = f\"{gpu_num}\"\n", + "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n", + "\n", + "# Import Sionna\n", + "try:\n", + " import sionna.phy\n", + "except ImportError as e:\n", + " import sys\n", + " if 'google.colab' in sys.modules:\n", + " # Install Sionna in Google Colab\n", + " print(\"Installing Sionna and restarting the runtime. Please run the cell again.\")\n", + " os.system(\"pip install sionna\")\n", + " os.kill(os.getpid(), 5)\n", + " else:\n", + " raise e\n", + "\n", + "# Configure the notebook to use only a single GPU and allocate only as much memory as needed\n", + "# For more details, see https://www.tensorflow.org/guide/gpu\n", + "import tensorflow as tf\n", + "gpus = tf.config.list_physical_devices('GPU')\n", + "if gpus:\n", + " try:\n", + " tf.config.experimental.set_memory_growth(gpus[0], True)\n", + " except RuntimeError as e:\n", + " print(e)\n", + "# Avoid warnings from TensorFlow\n", + "tf.get_logger().setLevel('ERROR')\n", + "\n", + "sionna.phy.config.seed = 42 # Set seed for reproducible random number generation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:45.903639Z", + "iopub.status.busy": "2025-03-13T01:48:45.903247Z", + "iopub.status.idle": "2025-03-13T01:48:45.914833Z", + "shell.execute_reply": "2025-03-13T01:48:45.913951Z" + }, + "id": "kZ21nyrJtPmJ" + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Load the required Sionna components\n", + "from sionna.phy import Block\n", + "from sionna.phy.mimo import StreamManagement\n", + "from sionna.phy.ofdm import ResourceGrid, ResourceGridMapper, LSChannelEstimator, \\\n", + " LMMSEEqualizer, RZFPrecoder, RemoveNulledSubcarriers\n", + "from sionna.phy.channel import subcarrier_frequencies, ApplyOFDMChannel, \\\n", + " GenerateOFDMChannel, CIRDataset\n", + "from sionna.phy.fec.ldpc import LDPC5GEncoder, LDPC5GDecoder\n", + "from sionna.phy.mapping import BinarySource, Mapper, Demapper\n", + "from sionna.phy.utils import ebnodb2no, sim_ber\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6Ze8zPzztPmJ" + }, + "source": [ + "## Configuration of DeepMIMO" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "12rSAfnUtPmJ" + }, + "source": [ + "DeepMIMO provides multiple [scenarios](https://deepmimo.net/scenarios/) that one can select from. Here, we use the `O1_60` scenario, which is an instance of O1 with the carrier frequency set to 60 GHz.\n", + "\n", + "To use DeepMIMO with Sionna, the DeepMIMO dataset first needs to be generated. The generated DeepMIMO dataset contains channels for different locations of the users and basestations. The layout of the O1 scenario is shown in the figure below.\n", + "\n", + "![img](https://deepmimo.net/examples/sionna_adapter_example.png)\n", + "\n", + "In this example, we generate a dataset that consists of channels for the links from the basestation 6 to the users located on the rows 400 to 450. Each of these rows consists of 181 user locations, resulting in $51 \\times 181 = 9231$ basestation-user channels.\n", + "\n", + "The antenna arrays in the DeepMIMO dataset are defined as number of horizontal & vertical elements. In the following example, a single-user MISO downlink is considered. The basestation is equipped with a uniform linear array of 16 elements, which is oriented towards the positive x-axis by default. The users are each equipped with a single antenna. These parameters can be configured using the code below (for more information about the DeepMIMO parameters, please check [the DeepMIMO documentation](https://deepmimo.net/documentation)." + ] + }, + { + "cell_type": "code", + "source": [ + "# Import DeepMIMO (install package if import fails)\n", + "try:\n", + " import deepmimo as dm\n", + "except ImportError as e:\n", + " import os\n", + " os.system(\"pip install --pre deepmimo\")\n", + " import deepmimo as dm" + ], + "metadata": { + "id": "Y39zbDbyuQ2J" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:45.918949Z", + "iopub.status.busy": "2025-03-13T01:48:45.918713Z", + "iopub.status.idle": "2025-03-13T01:48:52.133244Z", + "shell.execute_reply": "2025-03-13T01:48:52.132383Z" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "h8cNH5cGtPmN", + "outputId": "edf9507e-66a9-47f8-cd14-e3f977c3f0d0" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading scenario 'o1_60'\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Downloading: 100%|██████████| 2.30G/2.30G [00:45<00:00, 54.6MB/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "✓ Downloaded to /content/deepmimo_scenarios/o1_60_downloaded.zip\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Extracting: 100%|██████████| 4161/4161 [02:37<00:00, 26.34file/s] \n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "✓ Unzipped and moved to /content/deepmimo_scenarios\n", + "✓ Scenario 'o1_60' ready to use!\n", + "Loading TXRX PAIR: TXset 6 (tx_idx 0) & RXset 0 (rx_idxs 497931)\n" + ] + } + ], + "source": [ + "# Download the dataset\n", + "dm.download('O1_60')\n", + "\n", + "# Load a BS-RX grid combination\n", + "load_params = {'max_paths': 10, 'tx_sets': [6], 'rx_sets': [0]}\n", + "dataset = dm.load('O1_60', **load_params)\n", + "\n", + "# Select a subset of users in the dataset (and trim matrices)\n", + "sel_usr_idxs = dataset.get_row_idxs(range(400, 451))\n", + "dataset_t = dataset.subset(sel_usr_idxs)" + ] + }, + { + "cell_type": "code", + "source": [ + "# Channel generation\n", + "params = dm.ChannelParameters() # Load the default parameters\n", + "\n", + "# Configuration of the antenna arrays\n", + "params.bs_antenna.shape = [16, 1] # BS antenna shape [horizontal, vertical]\n", + "params.ue_antenna.shape = [1, 1] # UE antenna shape [horizontal, vertical]\n", + "\n", + "# Configure time domain channels. Sionna will generate in frequency domain\n", + "params.freq_domain = False\n", + "\n", + "# Generates a DeepMIMO dataset\n", + "dataset_t.compute_channels(params)\n", + "dataset_t.channels.shape # (num_rx, num_rx_ant, num_tx_ant, num_time)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XkPe8gvwuPfk", + "outputId": "fe0fe508-7a82-4639-d46c-86c5b0be6967" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Generating channels: 100%|██████████| 9231/9231 [00:00<00:00, 29525.95it/s]\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(9231, 1, 16, 10)" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dhOdsh0rtPmN" + }, + "source": [ + "### Visualization of the dataset\n", + "\n", + "To provide a better understanding of the user and basestation locations, we next visualize the locations of the users, highlighting the first active row of users (row 400), and basestation 6." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:52.136681Z", + "iopub.status.busy": "2025-03-13T01:48:52.136424Z", + "iopub.status.idle": "2025-03-13T01:48:52.372533Z", + "shell.execute_reply": "2025-03-13T01:48:52.371889Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 388 + }, + "id": "0ShXyVmItPmN", + "outputId": "0b9b8a8c-4076-408e-9027-cf0160c3a797" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + " # Plot the azimuth of the AoA\n", + "dataset_t.aoa_az.plot(cbar_title='Angle of Arrival (deg)')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YlwUGc-NtPmO" + }, + "source": [ + "## Using DeepMIMO with Sionna\n", + "\n", + "The DeepMIMO Python package provides [a Sionna-compliant channel impulse response generator](https://nvlabs.github.io/sionna/phy/tutorials/CIR_Dataset.html#Generators) that adapts the structure of the DeepMIMO dataset to be consistent with Sionna.\n", + "\n", + "An adapter is instantiated for a given DeepMIMO dataset. The adapter only requires the dataset to adapt and it will generate samples for all users and basestations in that dataset.\n", + "\n", + "In this example, `SionnaAdapter(dataset)` will return the channels from the basestation 6 and the 9231 available user locations.\n", + "\n", + "**Note:** The adapter assumes basestations are transmitters and users are receivers. Uplink channels can be obtained using (transpose) reciprocity." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:52.376377Z", + "iopub.status.busy": "2025-03-13T01:48:52.376179Z", + "iopub.status.idle": "2025-03-13T01:48:52.380757Z", + "shell.execute_reply": "2025-03-13T01:48:52.380192Z" + }, + "id": "6LDSxX9PtPmO" + }, + "outputs": [], + "source": [ + "from deepmimo.integrations import SionnaAdapter\n", + "\n", + "deepmimo_sionna_adapter = SionnaAdapter(dataset_t)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rl8wZ1O-tPmO" + }, + "source": [ + "## Link-level Simulations using Sionna and DeepMIMO\n", + "\n", + "In the following cell, we define a Sionna model implementing the end-to-end link.\n", + "\n", + "**Note:** The Sionna CIRDataset object shuffles the DeepMIMO channels provided by the adapter. Therefore, channel samples are passed through the model in a random order." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:52.384454Z", + "iopub.status.busy": "2025-03-13T01:48:52.384238Z", + "iopub.status.idle": "2025-03-13T01:48:52.397002Z", + "shell.execute_reply": "2025-03-13T01:48:52.396376Z" + }, + "id": "EeoY1rP9tPmO" + }, + "outputs": [], + "source": [ + "class LinkModel(Block):\n", + " def __init__(self,\n", + " DeepMIMO_Sionna_adapter,\n", + " carrier_frequency,\n", + " cyclic_prefix_length,\n", + " pilot_ofdm_symbol_indices,\n", + " subcarrier_spacing = 60e3,\n", + " batch_size = 64\n", + " ):\n", + " super().__init__()\n", + "\n", + " self._batch_size = batch_size\n", + " self._cyclic_prefix_length = cyclic_prefix_length\n", + " self._pilot_ofdm_symbol_indices = pilot_ofdm_symbol_indices\n", + "\n", + " # CIRDataset to parse the dataset\n", + " self._CIR = CIRDataset(DeepMIMO_Sionna_adapter,\n", + " self._batch_size,\n", + " DeepMIMO_Sionna_adapter.num_rx,\n", + " DeepMIMO_Sionna_adapter.num_rx_ant,\n", + " DeepMIMO_Sionna_adapter.num_tx,\n", + " DeepMIMO_Sionna_adapter.num_tx_ant,\n", + " DeepMIMO_Sionna_adapter.num_paths,\n", + " DeepMIMO_Sionna_adapter.num_time_steps)\n", + "\n", + " # System parameters\n", + " self._carrier_frequency = carrier_frequency\n", + " self._subcarrier_spacing = subcarrier_spacing\n", + " self._fft_size = 76\n", + " self._num_ofdm_symbols = 14\n", + " self._num_streams_per_tx = DeepMIMO_Sionna_adapter.num_rx\n", + " self._dc_null = False\n", + " self._num_guard_carriers = [0, 0]\n", + " self._pilot_pattern = \"kronecker\"\n", + " self._pilot_ofdm_symbol_indices = pilot_ofdm_symbol_indices\n", + " self._num_bits_per_symbol = 4\n", + " self._coderate = 0.5\n", + "\n", + " # Setup the OFDM resource grid and stream management\n", + " self._sm = StreamManagement(np.ones([DeepMIMO_Sionna_adapter.num_rx, 1], int), self._num_streams_per_tx)\n", + " self._rg = ResourceGrid(num_ofdm_symbols=self._num_ofdm_symbols,\n", + " fft_size=self._fft_size,\n", + " subcarrier_spacing = self._subcarrier_spacing,\n", + " num_tx=DeepMIMO_Sionna_adapter.num_tx,\n", + " num_streams_per_tx=self._num_streams_per_tx,\n", + " cyclic_prefix_length=self._cyclic_prefix_length,\n", + " num_guard_carriers=self._num_guard_carriers,\n", + " dc_null=self._dc_null,\n", + " pilot_pattern=self._pilot_pattern,\n", + " pilot_ofdm_symbol_indices=self._pilot_ofdm_symbol_indices)\n", + "\n", + " # Components forming the link\n", + "\n", + " # Codeword length\n", + " self._n = int(self._rg.num_data_symbols * self._num_bits_per_symbol)\n", + " # Number of information bits per codeword\n", + " self._k = int(self._n * self._coderate)\n", + "\n", + " # OFDM channel\n", + " self._frequencies = subcarrier_frequencies(self._rg.fft_size, self._rg.subcarrier_spacing)\n", + " self._ofdm_channel = GenerateOFDMChannel(self._CIR, self._rg, normalize_channel=True)\n", + " self._channel_freq = ApplyOFDMChannel(add_awgn=True)\n", + "\n", + " # Transmitter\n", + " self._binary_source = BinarySource()\n", + " self._encoder = LDPC5GEncoder(self._k, self._n)\n", + " self._mapper = Mapper(\"qam\", self._num_bits_per_symbol)\n", + " self._rg_mapper = ResourceGridMapper(self._rg)\n", + " self._zf_precoder = RZFPrecoder(self._rg, self._sm, return_effective_channel=True)\n", + "\n", + " # Receiver\n", + " self._ls_est = LSChannelEstimator(self._rg, interpolation_type=\"lin_time_avg\")\n", + " self._lmmse_equ = LMMSEEqualizer(self._rg, self._sm)\n", + " self._demapper = Demapper(\"app\", \"qam\", self._num_bits_per_symbol)\n", + " self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n", + " self._remove_nulled_scs = RemoveNulledSubcarriers(self._rg)\n", + "\n", + " def call(self, batch_size, ebno_db):\n", + "\n", + " # Transmitter\n", + " b = self._binary_source([self._batch_size, 1, self._num_streams_per_tx, self._k])\n", + " c = self._encoder(b)\n", + " x = self._mapper(c)\n", + " x_rg = self._rg_mapper(x)\n", + " # Generate the OFDM channel\n", + " h_freq = self._ofdm_channel()\n", + " # Precoding\n", + " x_rg, g = self._zf_precoder(x_rg, h_freq)\n", + "\n", + " # Apply OFDM channel\n", + " no = ebnodb2no(ebno_db, self._num_bits_per_symbol, self._coderate, self._rg)\n", + " y = self._channel_freq(x_rg, h_freq, no)\n", + "\n", + " # Receiver\n", + " h_hat, err_var = self._ls_est (y, no)\n", + " x_hat, no_eff = self._lmmse_equ(y, h_hat, err_var, no)\n", + " llr = self._demapper(x_hat, no_eff)\n", + " b_hat = self._decoder(llr)\n", + "\n", + " return b, b_hat" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "15OdNJoLtPmO" + }, + "source": [ + "We next evaluate the setup with different $E_b/N_0$ values to obtain BLER curves." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:52.400127Z", + "iopub.status.busy": "2025-03-13T01:48:52.399905Z", + "iopub.status.idle": "2025-03-13T01:49:23.855650Z", + "shell.execute_reply": "2025-03-13T01:49:23.854765Z" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WC1tSAq9tPmP", + "outputId": "26a07782-c588-4a32-c706-364f58b3dfe6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " -7.0 | 1.2781e-01 | 1.0000e+00 | 29840 | 233472 | 128 | 128 | 7.4 |reached target block errors\n", + " -6.806 | 9.7352e-02 | 1.0000e+00 | 22729 | 233472 | 128 | 128 | 1.0 |reached target block errors\n", + " -6.611 | 6.6376e-02 | 9.4531e-01 | 15497 | 233472 | 121 | 128 | 1.0 |reached target block errors\n", + " -6.417 | 3.0492e-02 | 7.9688e-01 | 7119 | 233472 | 102 | 128 | 1.0 |reached target block errors\n", + " -6.222 | 1.1498e-02 | 4.7656e-01 | 5369 | 466944 | 122 | 256 | 2.1 |reached target block errors\n", + " -6.028 | 1.4813e-03 | 1.3281e-01 | 2075 | 1400832 | 102 | 768 | 6.8 |reached target block errors\n", + " -5.833 | 1.6177e-04 | 2.2645e-02 | 1303 | 8054784 | 100 | 4416 | 38.1 |reached target block errors\n", + " -5.639 | 1.4563e-05 | 3.9062e-03 | 170 | 11673600 | 25 | 6400 | 56.0 |reached max iterations\n", + " -5.444 | 3.5979e-06 | 6.2500e-04 | 42 | 11673600 | 4 | 6400 | 54.6 |reached max iterations\n", + " -5.25 | 0.0000e+00 | 0.0000e+00 | 0 | 11673600 | 0 | 6400 | 57.5 |reached max iterations\n", + "\n", + "Simulation stopped as no error occurred @ EbNo = -5.2 dB.\n", + "\n" + ] + } + ], + "source": [ + "sim_params = {\"ebno_db\": np.linspace(-7, -5.25, 10),\n", + " \"cyclic_prefix_length\" : 0,\n", + " \"pilot_ofdm_symbol_indices\" : [2, 11]}\n", + "\n", + "batch_size = 64\n", + "model = LinkModel(DeepMIMO_Sionna_adapter=deepmimo_sionna_adapter,\n", + " carrier_frequency=dataset_t.rt_params.frequency,\n", + " cyclic_prefix_length=sim_params[\"cyclic_prefix_length\"],\n", + " pilot_ofdm_symbol_indices=sim_params[\"pilot_ofdm_symbol_indices\"])\n", + "\n", + "ber, bler = sim_ber(model,\n", + " sim_params[\"ebno_db\"],\n", + " batch_size=batch_size,\n", + " max_mc_iter=100,\n", + " num_target_block_errors=100,\n", + " graph_mode=\"graph\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:49:23.859850Z", + "iopub.status.busy": "2025-03-13T01:49:23.859631Z", + "iopub.status.idle": "2025-03-13T01:49:24.255402Z", + "shell.execute_reply": "2025-03-13T01:49:24.254518Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 716 + }, + "id": "q5G8_EU6tPmP", + "outputId": "8c3bffcc-d47d-4cde-9c65-0f6c984ea3cb" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[]" + ] + }, + "metadata": {}, + "execution_count": 11 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "plt.figure(figsize=(12,8))\n", + "plt.xlabel(r\"$E_b/N_0$ (dB)\")\n", + "plt.ylabel(\"BLER\")\n", + "plt.grid(which=\"both\")\n", + "plt.semilogy(sim_params[\"ebno_db\"], bler)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MY82CFF9tPmP" + }, + "source": [ + "## DeepMIMO License and Citation\n", + "\n", + "A. Alkhateeb, “[DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://arxiv.org/pdf/1902.06435.pdf),” in Proc. of Information Theory and Applications Workshop (ITA), San Diego, CA, Feb. 2019.\n", + "\n", + "To use the DeepMIMO dataset, please check the license information [here](https://deepmimo.net/license/)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + }, + "colab": { + "provenance": [] + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From 35671414c1e8cf83727fcfcd090fbb271c746361 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Morais?= <19170303+jmoraispk@users.noreply.github.com> Date: Wed, 2 Jul 2025 17:03:29 -0400 Subject: [PATCH 5/7] rename notebook - does it got to the right place? --- DeepMIMOv4.ipynb | 635 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 635 insertions(+) create mode 100644 DeepMIMOv4.ipynb diff --git a/DeepMIMOv4.ipynb b/DeepMIMOv4.ipynb new file mode 100644 index 000000000..c35001783 --- /dev/null +++ b/DeepMIMOv4.ipynb @@ -0,0 +1,635 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "lSbcn0aLtPmH" + }, + "source": [ + "# Using the DeepMIMO Dataset with Sionna\n", + "\n", + "In this example, you will learn how to use the ray-tracing based DeepMIMO dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Thv9PInhtPmI" + }, + "source": [ + "[DeepMIMO](https://deepmimo.net/) is a generic dataset that enables a wide range of machine/deep learning applications for MIMO systems. It takes as input a set of parameters (such as antenna array configurations and time-domain/OFDM parameters) and generates MIMO channel realizations, corresponding locations, angles of arrival/departure, etc., based on these parameters and on a ray-tracing scenario selected [from those available in DeepMIMO](https://deepmimo.net/scenarios/).\n", + "\n", + "## Table of Contents\n", + "* [GPU Configuration and Imports](#GPU-Configuration-and-Imports)\n", + "* [Configuration of DeepMIMO](#Configuration-of-DeepMIMO)\n", + "* [Using DeepMIMO with Sionna](#Using-DeepMIMO-with-Sionna)\n", + "* [Link-level Simulations using Sionna and DeepMIMO](#Link-level-Simulations-using-Sionna-and-DeepMIMO)\n", + "* [DeepMIMO License and Citation](#DeepMIMO-License-and-Citation)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G-qy_3RatPmI" + }, + "source": [ + "## GPU Configuration and Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:42.830858Z", + "iopub.status.busy": "2025-03-13T01:48:42.830249Z", + "iopub.status.idle": "2025-03-13T01:48:45.899584Z", + "shell.execute_reply": "2025-03-13T01:48:45.898771Z" + }, + "id": "yuejsob6tPmJ" + }, + "outputs": [], + "source": [ + "import os\n", + "if os.getenv(\"CUDA_VISIBLE_DEVICES\") is None:\n", + " gpu_num = 0 # Use \"\" to use the CPU\n", + " os.environ[\"CUDA_VISIBLE_DEVICES\"] = f\"{gpu_num}\"\n", + "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n", + "\n", + "# Import Sionna\n", + "try:\n", + " import sionna.phy\n", + "except ImportError as e:\n", + " import sys\n", + " if 'google.colab' in sys.modules:\n", + " # Install Sionna in Google Colab\n", + " print(\"Installing Sionna and restarting the runtime. Please run the cell again.\")\n", + " os.system(\"pip install sionna\")\n", + " os.kill(os.getpid(), 5)\n", + " else:\n", + " raise e\n", + "\n", + "# Configure the notebook to use only a single GPU and allocate only as much memory as needed\n", + "# For more details, see https://www.tensorflow.org/guide/gpu\n", + "import tensorflow as tf\n", + "gpus = tf.config.list_physical_devices('GPU')\n", + "if gpus:\n", + " try:\n", + " tf.config.experimental.set_memory_growth(gpus[0], True)\n", + " except RuntimeError as e:\n", + " print(e)\n", + "# Avoid warnings from TensorFlow\n", + "tf.get_logger().setLevel('ERROR')\n", + "\n", + "sionna.phy.config.seed = 42 # Set seed for reproducible random number generation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:45.903639Z", + "iopub.status.busy": "2025-03-13T01:48:45.903247Z", + "iopub.status.idle": "2025-03-13T01:48:45.914833Z", + "shell.execute_reply": "2025-03-13T01:48:45.913951Z" + }, + "id": "kZ21nyrJtPmJ" + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Load the required Sionna components\n", + "from sionna.phy import Block\n", + "from sionna.phy.mimo import StreamManagement\n", + "from sionna.phy.ofdm import ResourceGrid, ResourceGridMapper, LSChannelEstimator, \\\n", + " LMMSEEqualizer, RZFPrecoder, RemoveNulledSubcarriers\n", + "from sionna.phy.channel import subcarrier_frequencies, ApplyOFDMChannel, \\\n", + " GenerateOFDMChannel, CIRDataset\n", + "from sionna.phy.fec.ldpc import LDPC5GEncoder, LDPC5GDecoder\n", + "from sionna.phy.mapping import BinarySource, Mapper, Demapper\n", + "from sionna.phy.utils import ebnodb2no, sim_ber\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6Ze8zPzztPmJ" + }, + "source": [ + "## Configuration of DeepMIMO" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "12rSAfnUtPmJ" + }, + "source": [ + "DeepMIMO provides multiple [scenarios](https://deepmimo.net/scenarios/) that one can select from. Here, we use the `O1_60` scenario, which is an instance of O1 with the carrier frequency set to 60 GHz.\n", + "\n", + "To use DeepMIMO with Sionna, the DeepMIMO dataset first needs to be generated. The generated DeepMIMO dataset contains channels for different locations of the users and basestations. The layout of the O1 scenario is shown in the figure below.\n", + "\n", + "![img](https://deepmimo.net/examples/sionna_adapter_example.png)\n", + "\n", + "In this example, we generate a dataset that consists of channels for the links from the basestation 6 to the users located on the rows 400 to 450. Each of these rows consists of 181 user locations, resulting in $51 \\times 181 = 9231$ basestation-user channels.\n", + "\n", + "The antenna arrays in the DeepMIMO dataset are defined as number of horizontal & vertical elements. In the following example, a single-user MISO downlink is considered. The basestation is equipped with a uniform linear array of 16 elements, which is oriented towards the positive x-axis by default. The users are each equipped with a single antenna. These parameters can be configured using the code below (for more information about the DeepMIMO parameters, please check [the DeepMIMO documentation](https://deepmimo.net/documentation)." + ] + }, + { + "cell_type": "code", + "source": [ + "# Import DeepMIMO (install package if import fails)\n", + "try:\n", + " import deepmimo as dm\n", + "except ImportError as e:\n", + " import os\n", + " os.system(\"pip install --pre deepmimo\")\n", + " import deepmimo as dm" + ], + "metadata": { + "id": "Y39zbDbyuQ2J" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:45.918949Z", + "iopub.status.busy": "2025-03-13T01:48:45.918713Z", + "iopub.status.idle": "2025-03-13T01:48:52.133244Z", + "shell.execute_reply": "2025-03-13T01:48:52.132383Z" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "h8cNH5cGtPmN", + "outputId": "edf9507e-66a9-47f8-cd14-e3f977c3f0d0" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading scenario 'o1_60'\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Downloading: 100%|██████████| 2.30G/2.30G [00:45<00:00, 54.6MB/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "✓ Downloaded to /content/deepmimo_scenarios/o1_60_downloaded.zip\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Extracting: 100%|██████████| 4161/4161 [02:37<00:00, 26.34file/s] \n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "✓ Unzipped and moved to /content/deepmimo_scenarios\n", + "✓ Scenario 'o1_60' ready to use!\n", + "Loading TXRX PAIR: TXset 6 (tx_idx 0) & RXset 0 (rx_idxs 497931)\n" + ] + } + ], + "source": [ + "# Download the dataset\n", + "dm.download('O1_60')\n", + "\n", + "# Load a BS-RX grid combination\n", + "load_params = {'max_paths': 10, 'tx_sets': [6], 'rx_sets': [0]}\n", + "dataset = dm.load('O1_60', **load_params)\n", + "\n", + "# Select a subset of users in the dataset (and trim matrices)\n", + "sel_usr_idxs = dataset.get_row_idxs(range(400, 451))\n", + "dataset_t = dataset.subset(sel_usr_idxs)" + ] + }, + { + "cell_type": "code", + "source": [ + "# Channel generation\n", + "params = dm.ChannelParameters() # Load the default parameters\n", + "\n", + "# Configuration of the antenna arrays\n", + "params.bs_antenna.shape = [16, 1] # BS antenna shape [horizontal, vertical]\n", + "params.ue_antenna.shape = [1, 1] # UE antenna shape [horizontal, vertical]\n", + "\n", + "# Configure time domain channels. Sionna will generate in frequency domain\n", + "params.freq_domain = False\n", + "\n", + "# Generates a DeepMIMO dataset\n", + "dataset_t.compute_channels(params)\n", + "dataset_t.channels.shape # (num_rx, num_rx_ant, num_tx_ant, num_time)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XkPe8gvwuPfk", + "outputId": "fe0fe508-7a82-4639-d46c-86c5b0be6967" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Generating channels: 100%|██████████| 9231/9231 [00:00<00:00, 29525.95it/s]\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(9231, 1, 16, 10)" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dhOdsh0rtPmN" + }, + "source": [ + "### Visualization of the dataset\n", + "\n", + "To provide a better understanding of the user and basestation locations, we next visualize the locations of the users, highlighting the first active row of users (row 400), and basestation 6." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:52.136681Z", + "iopub.status.busy": "2025-03-13T01:48:52.136424Z", + "iopub.status.idle": "2025-03-13T01:48:52.372533Z", + "shell.execute_reply": "2025-03-13T01:48:52.371889Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 388 + }, + "id": "0ShXyVmItPmN", + "outputId": "0b9b8a8c-4076-408e-9027-cf0160c3a797" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + " # Plot the azimuth of the AoA\n", + "dataset_t.aoa_az.plot(cbar_title='Angle of Arrival (deg)')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YlwUGc-NtPmO" + }, + "source": [ + "## Using DeepMIMO with Sionna\n", + "\n", + "The DeepMIMO Python package provides [a Sionna-compliant channel impulse response generator](https://nvlabs.github.io/sionna/phy/tutorials/CIR_Dataset.html#Generators) that adapts the structure of the DeepMIMO dataset to be consistent with Sionna.\n", + "\n", + "An adapter is instantiated for a given DeepMIMO dataset. The adapter only requires the dataset to adapt and it will generate samples for all users and basestations in that dataset.\n", + "\n", + "In this example, `SionnaAdapter(dataset)` will return the channels from the basestation 6 and the 9231 available user locations.\n", + "\n", + "**Note:** The adapter assumes basestations are transmitters and users are receivers. Uplink channels can be obtained using (transpose) reciprocity." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:52.376377Z", + "iopub.status.busy": "2025-03-13T01:48:52.376179Z", + "iopub.status.idle": "2025-03-13T01:48:52.380757Z", + "shell.execute_reply": "2025-03-13T01:48:52.380192Z" + }, + "id": "6LDSxX9PtPmO" + }, + "outputs": [], + "source": [ + "from deepmimo.integrations import SionnaAdapter\n", + "\n", + "deepmimo_sionna_adapter = SionnaAdapter(dataset_t)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rl8wZ1O-tPmO" + }, + "source": [ + "## Link-level Simulations using Sionna and DeepMIMO\n", + "\n", + "In the following cell, we define a Sionna model implementing the end-to-end link.\n", + "\n", + "**Note:** The Sionna CIRDataset object shuffles the DeepMIMO channels provided by the adapter. Therefore, channel samples are passed through the model in a random order." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:52.384454Z", + "iopub.status.busy": "2025-03-13T01:48:52.384238Z", + "iopub.status.idle": "2025-03-13T01:48:52.397002Z", + "shell.execute_reply": "2025-03-13T01:48:52.396376Z" + }, + "id": "EeoY1rP9tPmO" + }, + "outputs": [], + "source": [ + "class LinkModel(Block):\n", + " def __init__(self,\n", + " DeepMIMO_Sionna_adapter,\n", + " carrier_frequency,\n", + " cyclic_prefix_length,\n", + " pilot_ofdm_symbol_indices,\n", + " subcarrier_spacing = 60e3,\n", + " batch_size = 64\n", + " ):\n", + " super().__init__()\n", + "\n", + " self._batch_size = batch_size\n", + " self._cyclic_prefix_length = cyclic_prefix_length\n", + " self._pilot_ofdm_symbol_indices = pilot_ofdm_symbol_indices\n", + "\n", + " # CIRDataset to parse the dataset\n", + " self._CIR = CIRDataset(DeepMIMO_Sionna_adapter,\n", + " self._batch_size,\n", + " DeepMIMO_Sionna_adapter.num_rx,\n", + " DeepMIMO_Sionna_adapter.num_rx_ant,\n", + " DeepMIMO_Sionna_adapter.num_tx,\n", + " DeepMIMO_Sionna_adapter.num_tx_ant,\n", + " DeepMIMO_Sionna_adapter.num_paths,\n", + " DeepMIMO_Sionna_adapter.num_time_steps)\n", + "\n", + " # System parameters\n", + " self._carrier_frequency = carrier_frequency\n", + " self._subcarrier_spacing = subcarrier_spacing\n", + " self._fft_size = 76\n", + " self._num_ofdm_symbols = 14\n", + " self._num_streams_per_tx = DeepMIMO_Sionna_adapter.num_rx\n", + " self._dc_null = False\n", + " self._num_guard_carriers = [0, 0]\n", + " self._pilot_pattern = \"kronecker\"\n", + " self._pilot_ofdm_symbol_indices = pilot_ofdm_symbol_indices\n", + " self._num_bits_per_symbol = 4\n", + " self._coderate = 0.5\n", + "\n", + " # Setup the OFDM resource grid and stream management\n", + " self._sm = StreamManagement(np.ones([DeepMIMO_Sionna_adapter.num_rx, 1], int), self._num_streams_per_tx)\n", + " self._rg = ResourceGrid(num_ofdm_symbols=self._num_ofdm_symbols,\n", + " fft_size=self._fft_size,\n", + " subcarrier_spacing = self._subcarrier_spacing,\n", + " num_tx=DeepMIMO_Sionna_adapter.num_tx,\n", + " num_streams_per_tx=self._num_streams_per_tx,\n", + " cyclic_prefix_length=self._cyclic_prefix_length,\n", + " num_guard_carriers=self._num_guard_carriers,\n", + " dc_null=self._dc_null,\n", + " pilot_pattern=self._pilot_pattern,\n", + " pilot_ofdm_symbol_indices=self._pilot_ofdm_symbol_indices)\n", + "\n", + " # Components forming the link\n", + "\n", + " # Codeword length\n", + " self._n = int(self._rg.num_data_symbols * self._num_bits_per_symbol)\n", + " # Number of information bits per codeword\n", + " self._k = int(self._n * self._coderate)\n", + "\n", + " # OFDM channel\n", + " self._frequencies = subcarrier_frequencies(self._rg.fft_size, self._rg.subcarrier_spacing)\n", + " self._ofdm_channel = GenerateOFDMChannel(self._CIR, self._rg, normalize_channel=True)\n", + " self._channel_freq = ApplyOFDMChannel(add_awgn=True)\n", + "\n", + " # Transmitter\n", + " self._binary_source = BinarySource()\n", + " self._encoder = LDPC5GEncoder(self._k, self._n)\n", + " self._mapper = Mapper(\"qam\", self._num_bits_per_symbol)\n", + " self._rg_mapper = ResourceGridMapper(self._rg)\n", + " self._zf_precoder = RZFPrecoder(self._rg, self._sm, return_effective_channel=True)\n", + "\n", + " # Receiver\n", + " self._ls_est = LSChannelEstimator(self._rg, interpolation_type=\"lin_time_avg\")\n", + " self._lmmse_equ = LMMSEEqualizer(self._rg, self._sm)\n", + " self._demapper = Demapper(\"app\", \"qam\", self._num_bits_per_symbol)\n", + " self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n", + " self._remove_nulled_scs = RemoveNulledSubcarriers(self._rg)\n", + "\n", + " def call(self, batch_size, ebno_db):\n", + "\n", + " # Transmitter\n", + " b = self._binary_source([self._batch_size, 1, self._num_streams_per_tx, self._k])\n", + " c = self._encoder(b)\n", + " x = self._mapper(c)\n", + " x_rg = self._rg_mapper(x)\n", + " # Generate the OFDM channel\n", + " h_freq = self._ofdm_channel()\n", + " # Precoding\n", + " x_rg, g = self._zf_precoder(x_rg, h_freq)\n", + "\n", + " # Apply OFDM channel\n", + " no = ebnodb2no(ebno_db, self._num_bits_per_symbol, self._coderate, self._rg)\n", + " y = self._channel_freq(x_rg, h_freq, no)\n", + "\n", + " # Receiver\n", + " h_hat, err_var = self._ls_est (y, no)\n", + " x_hat, no_eff = self._lmmse_equ(y, h_hat, err_var, no)\n", + " llr = self._demapper(x_hat, no_eff)\n", + " b_hat = self._decoder(llr)\n", + "\n", + " return b, b_hat" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "15OdNJoLtPmO" + }, + "source": [ + "We next evaluate the setup with different $E_b/N_0$ values to obtain BLER curves." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:48:52.400127Z", + "iopub.status.busy": "2025-03-13T01:48:52.399905Z", + "iopub.status.idle": "2025-03-13T01:49:23.855650Z", + "shell.execute_reply": "2025-03-13T01:49:23.854765Z" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WC1tSAq9tPmP", + "outputId": "26a07782-c588-4a32-c706-364f58b3dfe6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " -7.0 | 1.2781e-01 | 1.0000e+00 | 29840 | 233472 | 128 | 128 | 7.4 |reached target block errors\n", + " -6.806 | 9.7352e-02 | 1.0000e+00 | 22729 | 233472 | 128 | 128 | 1.0 |reached target block errors\n", + " -6.611 | 6.6376e-02 | 9.4531e-01 | 15497 | 233472 | 121 | 128 | 1.0 |reached target block errors\n", + " -6.417 | 3.0492e-02 | 7.9688e-01 | 7119 | 233472 | 102 | 128 | 1.0 |reached target block errors\n", + " -6.222 | 1.1498e-02 | 4.7656e-01 | 5369 | 466944 | 122 | 256 | 2.1 |reached target block errors\n", + " -6.028 | 1.4813e-03 | 1.3281e-01 | 2075 | 1400832 | 102 | 768 | 6.8 |reached target block errors\n", + " -5.833 | 1.6177e-04 | 2.2645e-02 | 1303 | 8054784 | 100 | 4416 | 38.1 |reached target block errors\n", + " -5.639 | 1.4563e-05 | 3.9062e-03 | 170 | 11673600 | 25 | 6400 | 56.0 |reached max iterations\n", + " -5.444 | 3.5979e-06 | 6.2500e-04 | 42 | 11673600 | 4 | 6400 | 54.6 |reached max iterations\n", + " -5.25 | 0.0000e+00 | 0.0000e+00 | 0 | 11673600 | 0 | 6400 | 57.5 |reached max iterations\n", + "\n", + "Simulation stopped as no error occurred @ EbNo = -5.2 dB.\n", + "\n" + ] + } + ], + "source": [ + "sim_params = {\"ebno_db\": np.linspace(-7, -5.25, 10),\n", + " \"cyclic_prefix_length\" : 0,\n", + " \"pilot_ofdm_symbol_indices\" : [2, 11]}\n", + "\n", + "batch_size = 64\n", + "model = LinkModel(DeepMIMO_Sionna_adapter=deepmimo_sionna_adapter,\n", + " carrier_frequency=dataset_t.rt_params.frequency,\n", + " cyclic_prefix_length=sim_params[\"cyclic_prefix_length\"],\n", + " pilot_ofdm_symbol_indices=sim_params[\"pilot_ofdm_symbol_indices\"])\n", + "\n", + "ber, bler = sim_ber(model,\n", + " sim_params[\"ebno_db\"],\n", + " batch_size=batch_size,\n", + " max_mc_iter=100,\n", + " num_target_block_errors=100,\n", + " graph_mode=\"graph\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2025-03-13T01:49:23.859850Z", + "iopub.status.busy": "2025-03-13T01:49:23.859631Z", + "iopub.status.idle": "2025-03-13T01:49:24.255402Z", + "shell.execute_reply": "2025-03-13T01:49:24.254518Z" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 716 + }, + "id": "q5G8_EU6tPmP", + "outputId": "8c3bffcc-d47d-4cde-9c65-0f6c984ea3cb" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[]" + ] + }, + "metadata": {}, + "execution_count": 11 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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BnSP3/7+11dI7bgno2Pno2Pno2Nns1G8wGJTf769x0POzj1ryer0RX7pkn5yoPzp2tpba76Bu7fT/urXTlF8N0syftur1uRuUm79P0xZu1bSFWzW8RztdfnwPjRvSSTFRHqvjNkhL7bgloWPno2Pno2Nns0O/tc3HoAcARIzWMVG6/Pgeuiytuxas363X523UZ0u3a9HGPVq0cY/+9FG0Jh6XrIy07urWjodBAQCAlo1BDwCIOC6XS2m9E5XWO1E79pbo7QWblTl/k7YHS/T3r9fq+f+s1WkDOurykT10cl+/3G7nXUQPAACgJgx6AEBES2oTq5tP76ffj+qjL1bs0OvzNuj7Nbv0xYp8fbEiXz0SfbosrYcuOrab2vqirY4LAADQbBj0AABbiPK4NXZwJ40d3ElrduzTm/M3asaiLdq4K6RHPlmhpz5fpQkpXXTFyJ4a0i3B6rgAAABNjkEPALCdvkmt9cD4o3X7mP6alZ2n1+Zu1IptQU1ftEXTF21RSnJbXX58D509tLNivfa+iB4AAEB1GPQAANvyRUfpkhHd9evjkvXjpj16fe5GfbJku3I2Fypnc6Ee+Xi5Lj42WZem9VD3RC6iBwAAnIVBDwCwPZfLpeE92mt4j/a69+xSTfvhwEX0thbu1z+/Wad/fbtOo47qoMtH9tCpRyXJw0X0AACAAzDoAQCO4m8doz+M7qvfndpHX63codfmbdQ3uTv11aoDv5Lbt9KlaT108bHJah/HRfQAAIB9MehryTAMGYZhdYxqHcwWyRnRMHTsbPTbNE7t116n9muvjbtCylywWe/+tFWbd+/XY5+u1DNZufrV0R11aVqyUrolyOVq2p/a07Hz0bHz0bHz0bGz2anf2mZ0maZpNnEWWwoEAgoEAgqHw8rNzVVmZqZ8Ph5/CQB2VhaWftzl0nfb3dpc/N8B3y3O1MmdKnRMoqlorqEHAAAsFgqFlJGRoaKiIsXHx1d7PwZ9DYLBoBISElRQUHDET6TVDMNQVlaW0tPT5fV6rY6DJkDHzka/zS9nS5HeXLBZHy/ZrrLyCklSfGyULjimqzJGdFPPxLhGfX907Hx07Hx07Hx07Gx26jcYDMrv99c46Dnlvpa8Xm/Ely7ZJyfqj46djX6bz7G9/Dq2l1/3nV2m6Qs36435G7V5935NnbNRU+ds1Mn9/Lr8+B46fWDHRr2IHh07Hx07Hx07Hx07mx36rW0+Bj0AoEVrHxet357aR9ef3Fv/Wb1Tr8/dqK9W7dC3qwv07eoCdW3bShlp3XXxscnq0CbG6rgAAACVGPQAAEhyu10a3T9Jo/snafPukN6cv0nTfjjw1HdP/nuVnvsiV78a0lmXH99Dw3u0a/KL6AEAANSEQQ8AwP9Ibu/TlHEDNOmMfvpkyTa9NnejsjcXalZ2nmZl52lg53hdfnwPnZPaRXEx/FMKAACswVchAABUI9br0fnHdNP5x3TTki1FemPeRs3K2aoV24K6e+YS/fmTFbpgeDdddnwP9U1qbXVcAADQwritDgAAgB0M6Zagxy8cqvl3naF7zxqonok+7S0t1ytzNuiMZ/6jjBfm6dMl21QerrA6KgAAaCH4CT0AAHWQ4PPqupN765oTe+m7NQV6fd5GzV6Rrzlrd2nO2l3qFB+rS0Z01yUjkpUUH2t1XAAA4GAMegAA6sHtdumUozrolKM6aGvhfmXO36i3F2zW9mCJnv0iV3/9crXGDO6ky4/vobRe7a2OCwAAHIhBDwBAA3Vt20q3jxmgm0/vp8+Wbtcb8zbqhw179PHibfp48TYd1bG1Mo7rplblVicFAABOwqAHAKCRxER5dE5qV52T2lXL84J6Y/5Gvf/TVuXm79ODH61UjNujxa4VuuKEXurfqY3VcQEAgM1xUTwAAJrAoC7xevS8IZp39+l6cPwg9fbHqbTCpTcXbNaY577Rxf+cq48W56msnIvoAQCA+uEn9LVkGIYMw7A6RrUOZovkjGgYOnY2+nWuVh7p0hHddFFqkv7+7mytNjtp9qoCLVi/WwvW71aH1tG6+NhumnhsN3VO4CJ6dsZx7Hx07Hx07Gx26re2GV2maZpNnMWWAoGAAoGAwuGwcnNzlZmZKZ/PZ3UsAIADFJZKc3a4NTffpaDhkiS5ZWpwe1MndTJ1VLwpl8vikAAAwDKhUEgZGRkqKipSfHx8tfdj0NcgGAwqISFBBQUFR/xEWs0wDGVlZSk9PV1er9fqOGgCdOxs9Ot8h+vYCFfoixU79Mb8zVqwYU/lfXv7fcoYkazzUrsovhV/H+yC49j56Nj56NjZ7NRvMBiU3++vcdBzyn0teb3eiC9dsk9O1B8dOxv9Ot8vO/Z6pQnDkjVhWLJy8/fqjXkb9d6PW7WuIKT/98kqPZ21RucO66qbT++rzgmtLE6O2uI4dj46dj46djY79FvbfFwUDwCACHBUxzZ6+JzBmnf36frTuYN1VMfW2m+E9daCTbro+bnaWrjf6ogAACDCMOgBAIggrWOidPnxPfTvSafond+OVG9/nLbs2a9LX5in/GCJ1fEAAEAEYdADABCBXC6XRvRqrzevT1Ny+1basCukjBfmqWBfqdXRAABAhGDQAwAQwTontFLmdcerS0Ks1u4s1mUvztee4jKrYwEAgAjAoAcAIMIlt/cp8/rjldQmRiu379XlL89X0f7Ifw5dAADQtBj0AADYQE9/nDKvP17+1tFaujWoK19eoH2l5VbHAgAAFmLQAwBgE32TWuuN69LU1udV9uZCXTP1B4XKGPUAALRUDHoAAGxkQKd4vXFtmtrERmnBht267tWFKjHCVscCAAAWYNADAGAzg7sm6LVrRigu2qM5a3fpd28sUmk5ox4AgJaGQQ8AgA0N695OU68eoVZej75etVM3Zv4kI1xhdSwAANCMGPQAANjUiF7t9dKVxyomyq2s5fma9Ha2yhn1AAC0GAx6AABs7IS+fv3z8uGK9rj18ZJtun3GYoUrTKtjAQCAZsCgBwDA5kb1T9LfMoYpyu3SzJ+26p6ZS1TBqAcAwPGirA5gF4ZhyDAMq2NU62C2SM6IhqFjZ6Nf52vqjkcflahnLhqiSe8s1ts/bFaUW7r/rAFyuVxN8v5wKI5j56Nj56NjZ7NTv7XN6DJNk2/hH0YgEFAgEFA4HFZubq4yMzPl8/msjgUAwBH9sNOlN9e4ZcqlUZ0rdG6PCrHpAQCwl1AopIyMDBUVFSk+Pr7a+zHoaxAMBpWQkKCCgoIjfiKtZhiGsrKylJ6eLq/Xa3UcNAE6djb6db7m7Hj6oi26+/3lkqQbTu2lyWf0a9L3hwM4jp2Pjp2Pjp3NTv0Gg0H5/f4aBz2n3NeS1+uN+NIl++RE/dGxs9Gv8zVHxxnH91LYdOm+Wcv0j/+sly/aq5tOZ9Q3F45j56Nj56NjZ7NDv7XNx0XxAABwoMtH9tS9Zw2UJD2dlat//metxYkAAEBjY9ADAOBQ153cW7eP6S9J+vOnK/XK9+stTgQAABoTgx4AAAf7w+i+uvnn0+0f/HC5MudvsjgRAABoLAx6AAAc7o9n9NNvT+0tSbrn/SWasWiLxYkAAEBjYNADAOBwLpdLU8YO0FUn9JRpSnfMyNEHOXlWxwIAAA3EoAcAoAVwuVx6YPwgZaR1V4Up/XFatj5but3qWAAAoAEY9AAAtBAul0v/75zBuuCYbgpXmLrprR/15cp8q2MBAIB6YtADANCCuN0uPXHhUI1P6SIjbOp3b/yob1fvtDoWAACoBwY9AAAtjMft0jMXp2jM0R1VVl6h619bqHnrdlkdCwAA1BGDHgCAFsjrceuvlxyj0wYkqcSo0DWv/KBFG3dbHQsAANQBgx4AgBYqOsqtv196jE7q61eoLKyrXv5Bi7cUWh0LAADUEoMeAIAWLNbr0QtXHKsRvdprb2m5Ln9pgZbnBa2OBQAAaoFBDwBAC9cq2qOXrzpOx3Rvq6L9hi57ab5W5++1OhYAAKgBgx4AAKh1TJReuWaEhnZL0O7iMmW8OF/rdu6zOhYAADgCBj0AAJAkxcd69do1IzSgUxvt3FuqjBfma9OukNWxAABANRj0AACgUltftN68Lk39klpre7BEGS/O09bC/VbHAgAAh8GgBwAAVSS2jtGb16Wplz9OW/bs16UvzFN+sMTqWAAA4H8w6AEAwCGS4mOVeX2aktu30oZdIWW8ME8F+0qtjgUAAH4hyuoAdmEYhgzDsDpGtQ5mi+SMaBg6djb6dT47duz3Rem1q45Vxks/aO3OYl36wjy9fs2xaueLtjpaRLJjx6gbOnY+OnY2O/Vb24wu0zTNJs5iS4FAQIFAQOFwWLm5ucrMzJTP57M6FgAAzW7HfumvyzwKGi51izP1h0Fh+fiRAAAATSYUCikjI0NFRUWKj4+v9n4M+hoEg0ElJCSooKDgiJ9IqxmGoaysLKWnp8vr9VodB02Ajp2Nfp3P7h2v2bFPl778g3YXG0rplqBXrhqu1jGs+l+ye8eoGR07Hx07m536DQaD8vv9NQ56/iWuJa/XG/GlS/bJifqjY2ejX+eza8cDu7bTm9cdr0temKecLUX67RvZeuWa4+SL5kuJ/2XXjlF7dOx8dOxsdui3tvm4KB4AAKiVgZ3j9fo1aWoTG6UFG3brulcXqsQIWx0LAIAWi0EPAABqbUi3BL16zQjFRXs0Z+0u/e6NRSotZ9QDAGAFBj0AAKiTY7q309SrR6iV16OvV+3UjZk/yQhXWB0LAIAWh0EPAADqbESv9nrxymMVHeVW1vJ8TXo7W+WMegAAmhWDHgAA1MuJff365+XD5fW49PGSbbp9xmKFK3jyHAAAmguDHgAA1Nvo/kkKZByjKLdLM3/aqntmLlEFox4AgGbBoAcAAA1y5tGd9NyvU+V2SW//sFkPfrhMpsmoBwCgqTHoAQBAg509tIuevjhFLpf02tyNeuTjFYx6AACaGIMeAAA0ivOGddOfzxsiSXrxu/V6+vNcixMBAOBsDHoAANBofj2iux4+52hJ0t++WqO/zl5tcSIAAJyLQQ8AABrVFSN76t6zBkqSns7K1T//s9biRAAAOBODHgAANLrrTu6t28f0lyT9+dOVeuX79RYnAgDAeRj0AACgSfxhdF/dfFpfSdKDHy5X5vxNFicCAMBZGPQAAKDJ/DH9KP32lN6SpHveX6IZi7ZYnAgAAOdg0AMAgCbjcrk0ZdwAXXVCT5mmdMeMHH2Qk2d1LAAAHIFBDwAAmpTL5dID4wfpkhHdVWFKf5yWrc+Wbrc6FgAAtsegBwAATc7lcumRcwfrgmO6KVxh6qa3ftSXK/OtjgUAgK0x6AEAQLNwu1164sKhGp/SRUbY1O/e+FHfrt5pdSwAAGyLQQ8AAJqNx+3SMxenaMzRHVVWXqHrX1uoeet2WR0LAABbYtADAIBm5fW49ZdLhml0/w4qMSp0zSs/aNHG3VbHAgDAdhj0AACg2cVEefSPy4brpL5+hcrCuurlH7R4S6HVsQAAsBUGPQAAsESs16N/XTFcI3q1197Scl3+0gItzwtaHQsAANtg0AMAAMv4oqP08lXHaVj3tirab+iyl+Zrdf5eq2MBAGALDHoAAGCp1jFReuXqERrSNUG7i8uU8eJ8rdu5z+pYAABEPAY9AACwXEIrr167ZoQGdGqjnXtLlfHCfG3aFbI6FgAAEY1BDwAAIkK7uGi9cV2a+ia11vZgiTJenKethfutjgUAQMSKsjqAXRiGIcMwrI5RrYPZIjkjGoaOnY1+nY+Oaychxq1XrxqujBd/0MbdIWX8a57evPZYdYyPtTpajejY+ejY+ejY2ezUb20zukzTNJs4iy0FAgEFAgGFw2Hl5uYqMzNTPp/P6lgAALQIe0qlvyzzaHepSx1bmbrp6LDaeK1OBQBA8wiFQsrIyFBRUZHi4+OrvR+DvgbBYFAJCQkqKCg44ifSaoZhKCsrS+np6fJ6+YrHiejY2ejX+ei47jbvCSnjxR+0PViq/h1b6/VrjlU7X7TVsapFx85Hx85Hx85mp36DwaD8fn+Ng55T7mvJ6/VGfOmSfXKi/ujY2ejX+ei49nonJeit34zUxf+cq1X5+3TNaz/qzeuOV0KryP780bHz0bHz0bGz2aHf2ubjongAACBi9fLHKfO6NCXGRWvp1qCumrpA+0rLrY4FAEBEYNADAICI1q9jG71xXZra+rz6aVOhrpn6g0JljHoAABj0AAAg4g3sHK/Xr0lTm5goLdiwW9e/tlAlRtjqWAAAWIpBDwAAbGFItwS9cs0IxUV79P2aXbrhjUUqLWfUAwBaLgY9AACwjeE92unlq45TrNetr1bt1E2ZP8kIV1gdCwAASzDoAQCAraT1TtSLVxyn6Ci3Pl+er0nTslXOqAcAtEAMegAAYDsn9fPrn5cNl9fj0seLt+mOGYtVUWFaHQsAgGbFoAcAALY0ekCS/pZxjDxul977aavunrmEUQ8AaFEY9AAAwLbGHN1Jz01Mldslvf3DZj304TKZJqMeANAyMOgBAICtjU/poqcuSpHLJb06d6Me/WQFox4A0CIw6AEAgO2df0w3PXreEEnSC9+u1zNZuRYnAgCg6THoAQCAI1wyorsemnC0JOmvX67RX2evtjgRAABNi0EPAAAc48oTeuqeXw2UJD2dlat/fbPW4kQAADQdBj0AAHCU60/prdvOPEqS9OgnK/XqnA3WBgIAoIkw6AEAgOPceFo/3XRaX0nSAx8s01sLNlmcCACAxsegBwAAjjQ5/Sj95pTekqS7Zy7Ru4u2WJwIAIDGxaAHAACO5HK5dNe4AbpyZA+ZpnT7jBx9mJNndSwAABoNgx4AADiWy+XSA+OP1iUjklVhSpOmZeuzpdutjgUAQKNg0AMAAEdzu1165NwhOn9YV4UrTN301o/6auUOq2MBANBgDHoAAOB4brdLT1w4VGcP7SwjbOq3byzSd6sLrI4FAECDMOgBAECLEOVx69mJqTpzUEeVlVfoutd+0Px1u6yOBQBAvTHoAQBAi+H1uPXXjGEa1b+DSowKXfPKD1q0cY/VsQAAqBcGPQAAaFFiojx6/rLhOrFvoorLwrrq5QVavKXQ6lgAANQZgx4AALQ4sV6PXrjiWI3o2V57S8t1+UsLtDwvaHUsAADqhEEPAABaJF90lF6++jgN695WRfsNXf7SfK3O32t1LAAAao1BDwAAWqzWMVF65eoRGtw1XruKy5Tx4nytLyi2OhYAALXCoAcAAC1aQiuvXr8mTQM6tdHOvaXKeGGeNu8OWR0LAIAaMegBAECL1y4uWm9cl6Y+HeK0rahEl7wwT3mF+62OBQDAETHoAQAAJPlbxyjz+uPVM9GnLXv268bMH1UerrA6FgAA1WLQAwAA/KxjfKxevzZNbWKi9OOmQv3967VWRwIAoFoMegAAgF9Ibu/Tw+ceLUn6v9mrlbO50NpAAABUg0EPAADwP85N7aqzhnZWuMLUH6dlK1RWbnUkAAAOwaAHAAD4Hy6XS4+cO1id4mO1rqBYj36ywupIAAAcgkEPAABwGG190XrqohRJ0hvzNumrlTssTgQAQFUMegAAgGqc1M+vq0/sKUm6fcZi7dpXam0gAAB+gUEPAABwBHeOHaB+Sa1VsK9Ud723RKZpWh0JAABJDHoAAIAjivV69NyvU+X1uPT58nxNX7jF6kgAAEhi0AMAANTo6C4JuvXM/pKkhz5cpk27QhYnAgCAQQ8AAFAr15/cWyN6tVdxWVh/fCdb5eEKqyMBAFo4Bj0AAEAteNwuPXNxitrERGnRxj36x9drrY4EAGjhGPQAAAC11K2dTw+dc7Qk6f9mr9biLYXWBgIAtGgMegAAgDo4b1hXnTWks8orTE2alq39ZWGrIwEAWigGPQAAQB24XC49ct5gdYyP0bqdxXr0kxVWRwIAtFAMegAAgDpq64vWUxelSJJen7dRX63aYXEiAEBLxKAHAACoh5P7ddBVJ/SUJN0xY7F2F5dZGwgA0OK0iEF/3nnnqV27drrwwgutjgIAABxkyrgB6pfUWjv3lureWctlmlYnAgC0JC1i0N9yyy167bXXrI4BAAAcJtbr0bMTU+X1uJS1Yofm73RZHQkA0IK0iEE/atQotWnTxuoYAADAgQZ3TdDk9P6SpPfWu7Vpd8jiRACAlsLyQf/NN99o/Pjx6tKli1wul95///1D7hMIBNSzZ0/FxsYqLS1NCxYsaP6gAAAA1fjNKb11bI+2Kq1w6Y53l6o8XGF1JABACxBldYDi4mKlpKTommuu0fnnn3/Iy6dNm6bJkyfr+eefV1pamp577jmNGTNGq1atUlJSkiQpNTVV5eXlh7zu559/ri5dutQpT2lpqUpLSyv/HAwGJUmGYcgwjDq9reZ0MFskZ0TD0LGz0a/z0bHz/fmcATo7MFeLNhUq8OVq/X5Ub6sjoZFxHDsfHTubnfqtbUaXaUbO5VtcLpdmzpypc889t/K2tLQ0HXfccfrb3/4mSaqoqFBycrJuuukmTZkypdZv++uvv9bf/vY3zZgx44j3e/DBB/XQQw8dcntmZqZ8Pl+t3x8AAGh5Fux06c01Hrldpv44OKzura1OBACwo1AopIyMDBUVFSk+Pr7a+1n+E/ojKSsr06JFi3TXXXdV3uZ2u3XGGWdo7ty5TfI+77rrLk2ePLnyz8FgUMnJyTrzzDOP+Im0mmEYysrKUnp6urxer9Vx0ATo2Nno1/no2PkMw5D5eZZ2ejvo8xU7NXNbvN6/YaRaRXusjoZGwnHsfHTsbHbq9+CZ4jWJ6EFfUFCgcDisjh07Vrm9Y8eOWrlyZa3fzhlnnKGcnBwVFxerW7dumj59ukaOHHnY+8bExCgmJuaQ271eb8SXLtknJ+qPjp2Nfp2Pjp3N5ZL+37lHK3vLXK0rCOmpL9bo4XMGWx0LjYzj2Pno2Nns0G9t81l+Ubzm8MUXX2jnzp0KhULasmVLtWMeAACgodr5ovXURSmSpNfmbtTXq3ZYnAgA4FQRPej9fr88Ho/y8/Or3J6fn69OnTpZlAoAAODITjmqg646oack6fYZi7W7uMzaQAAAR4roQR8dHa3hw4dr9uzZlbdVVFRo9uzZ/JQdAABEtCnjBqhvUmvt3Fuqu99bogi6DjEAwCEsH/T79u1Tdna2srOzJUnr169Xdna2Nm3aJEmaPHmyXnjhBb366qtasWKFbrjhBhUXF+vqq6+2MDUAAMCRxXo9em5iqrwelz5btl0zFm2xOhIAwGEsvyjewoULNXr06Mo/H7zC/JVXXqlXXnlFEydO1M6dO3X//fdr+/btSk1N1WeffXbIhfKaGs9DD6vRsbPRr/PRsfMdruP+ST7dclpfPZW1Wg9+uEzDu8cruR1Pg2tXHMfOR8fOZqd+bfk89JEkEAgoEAgoHA4rNzeX56EHAAD1UmFKf1vm0dq9LvVqY+rmo8Nyu6xOBQCIZLV9HnoGfQ2CwaASEhJUUFDA89DDUnTsbPTrfHTsfEfqeMue/To7MEfFpWFNPqOvbji1t0Up0RAcx85Hx85mp36DwaD8fn+Ng97yU+7twg7PVSjZJyfqj46djX6dj46d73Ad90ry6qEJg3Xb9Bz95cu1Gj2gk4Z0S7AoIRqK49j56NjZ7NAvz0MPAAAQQS44pqvGDe6k8gpTk6b9pP1lYasjAQBsjkEPAADQDFwulx49b4iS2sRo7c5iPfbpCqsjAQBsjkEPAADQTNrFRevJi1IkSa/O3aj/5O60OBEAwM4Y9AAAAM3o1KM66MqRPSRJt0/P0Z7iMosTAQDsikEPAADQzKaMG6g+HeK0Y2+p7p65RDzpEACgPrjKfS0ZhiHDMKyOUa2D2SI5IxqGjp2Nfp2Pjp2vLh1HuaSnLxyiC/85X58u3a53ftio84d1beqIaCCOY+ejY2ezU7+1zcjz0FcjEAgoEAgoHA4rNzdXmZmZ8vl8VscCAAAOkrXVpY82eRTjMXXn0LASY61OBACIBKFQSBkZGTU+Dz2DvgbBYFAJCQkqKCg44ifSaoZhKCsrS+np6RH/nIqoHzp2Nvp1Pjp2vvp0HK4wddnLP2jhxkId26Ot3rjmOHncriZOivriOHY+OnY2O/UbDAbl9/trHPSccl9LXq834kuX7JMT9UfHzka/zkfHzleXjr2Snp04TOP+71st3Fiol+du0u9H9W3agGgwjmPno2Nns0O/tc3HRfEAAAAslNzepwfGD5IkPfN5rpZuLbI4EQDALhj0AAAAFrtweDeNPbqTyitMTZqWrRIjbHUkAIANMOgBAAAs5nK59Oj5Q9ShTYzW7Ninxz5daXUkAIANMOgBAAAiQPu4aD154VBJ0itzNuib3J0WJwIARDoGPQAAQIQY1T9JV4zsIUm6bXqO9hSXWZwIABDJGPQAAAAR5K5xA9WnQ5x27C3VPe8vEc8wDACoDk9bV0uGYcgwDKtjVOtgtkjOiIahY2ejX+ejY+drrI6jXNLTFw7Rhf+cr0+WbNf0HzbpvGFdGiMiGojj2Pno2Nns1G9tM7pMvu17WIFAQIFAQOFwWLm5ucrMzJTP57M6FgAAaCE+3+LSx5s9ivGYunNoWImxVicCADSXUCikjIwMFRUVKT4+vtr7MehrEAwGlZCQoIKCgiN+Iq1mGIaysrKUnp4ur9drdRw0ATp2Nvp1Pjp2vsbuOFxh6tKXftCiTYU6tkdbvXHNcfK4XY2QFPXFcex8dOxsduo3GAzK7/fXOOg55b6WvF5vxJcu2Scn6o+OnY1+nY+Ona+xOvZKeu7XwzT2uW+0cGOhps7drBtG9Wl4QDQYx7Hz0bGz2aHf2ubjongAAAARKrm9Tw9MOFqS9EzWKi3dWmRxIgBAJGHQAwAARLCLhnfTmKM7ygibmjQtWyVG2OpIAIAIwaAHAACIYC6XS38+f6g6tInRmh379NinK62OBACIEAx6AACACNc+LlpPXDhUkvTKnA36dvVOixMBACIBgx4AAMAGRvdP0uXH95Ak3TY9R4WhMosTAQCsxqAHAACwibt/NVC9O8QpP1iqe2YuFc8+DAAtG4MeAADAJlpFe/R/E4cpyu3Sx0u2aeZPW62OBACwEIMeAADARoZ0S9CkM/pJkh6YtUxb9oQsTgQAsEqU1QHswjAMGYZhdYxqHcwWyRnRMHTsbPTrfHTsfM3Z8bUndNeXK3fox02F+uO0bL1+9bHyuF1N/n5bOo5j56NjZ7NTv7XN6DJ58NVhBQIBBQIBhcNh5ebmKjMzUz6fz+pYAAAAkqSCEumJHI9KK1ya0D2s07vyJR0AOEUoFFJGRoaKiooUHx9f7f0Y9DUIBoNKSEhQQUHBET+RVjMMQ1lZWUpPT5fX67U6DpoAHTsb/TofHTufFR1PX7RVd7+/TF6PS9N/k6aju0Tu1ypOwHHsfHTsbHbqNxgMyu/31zjoOeW+lrxeb8SXLtknJ+qPjp2Nfp2Pjp2vOTu+JK2Hvs4t0OfL83X7u0v14U0nKdbraZb33ZJxHDsfHTubHfqtbT4uigcAAGBTLpdLfz5/iPytY7R6xz49/tlKqyMBAJoRgx4AAMDGElvH6MkLh0qSpn6/Qd+u3mlxIgBAc2HQAwAA2NzoAUm67PjukqTbpueoMFRmcSIAQHNg0AMAADjAPb8apN4d4pQfLNU97y8V1z0GAOdj0AMAADhAq2iPnpuYqii3Sx8v3qb3s7daHQkA0MQY9AAAAA4xtFtb3XJ6P0nS/e8v05Y9IYsTAQCaEoMeAADAQW4Y1UfHdG+rvaXluvWdHIUrOPUeAJyKQQ8AAOAgUR63np2Yqrhoj+av360Xv11ndSQAQBNh0AMAADhMj8Q43T9+kCTpqc9XaXle0OJEAICmwKAHAABwoIuPTdaZgzrKCJuaNO0nlRhhqyMBABpZlNUB7MIwDBmGYXWMah3MFskZ0TB07Gz063x07HyR2PHDEwbqx017lJu/T499slz3/GqA1ZFsLRI7RuOiY2ezU7+1zegyeZLSwwoEAgoEAgqHw8rNzVVmZqZ8Pp/VsQAAAOpk2R6X/rXSI0n6/cCw+rflSz8AiHShUEgZGRkqKipSfHx8tfdj0NcgGAwqISFBBQUFR/xEWs0wDGVlZSk9PV1er9fqOGgCdOxs9Ot8dOx8kdzxAx8uV+aCLeoYH6OP/nCC2voiK59dRHLHaBx07Gx26jcYDMrv99c46Dnlvpa8Xm/Ely7ZJyfqj46djX6dj46dLxI7vu/swZq3bo/WFRTroY9X6q+XDJPL5bI6lm1FYsdoXHTsbHbot7b5uCgeAACAw7WK9ujZiamKcrv00eJtmpWdZ3UkAEAjYNADAAC0ACnJbXXz6f0kSffNWqqthfstTgQAaCgGPQAAQAvx+1F9NKx7W+0tKdet72SrooJLKQGAnTHoAQAAWogoj1vPTUyVL9qjeet268Xv1lkdCQDQAAx6AACAFqRHYpzuP3uQJOmpf+dqeV7Q4kQAgPpi0AMAALQwE49L1hkDO6osXKFJ035SiRG2OhIAoB4Y9AAAAC2My+XSYxcMkb91tHLz9+nJf6+yOhIAoB4Y9AAAAC2Qv3WMnrhwqCTppe/W6/s1BRYnAgDUFYMeAACghTptQEddmtZdknTrOzkqChkWJwIA1AWDHgAAoAW756yB6uWP0/Zgie6dtdTqOACAOmDQAwAAtGC+6Cg9OzFVHrdLH+bkaVb2VqsjAQBqiUEPAADQwqUmt9XNp/WTJN37/lJtLdxvcSIAQG0w6AEAAKA/jO6jYd3bam9JuW57J0cVFabVkQAANYiyOoBdGIYhw4jcC8UczBbJGdEwdOxs9Ot8dOx8Tuj4yfMHa8Lf52ruul361zdrdO2JPa2OFFGc0DGOjI6dzU791jajyzRNvv16GIFAQIFAQOFwWLm5ucrMzJTP57M6FgAAQJOak+/StHUeeVymbh0SVtc4qxMBQMsTCoWUkZGhoqIixcfHV3s/Bn0NgsGgEhISVFBQcMRPpNUMw1BWVpbS09Pl9XqtjoMmQMfORr/OR8fO55SOTdPUDZnZmr1yp45Kaq33fpemGK/H6lgRwSkdo3p07Gx26jcYDMrv99c46Dnlvpa8Xm/Ely7ZJyfqj46djX6dj46dzwkdP35hisY+941yd+zTc1+u071nD7I6UkRxQsc4Mjp2Njv0W9t8XBQPAAAAVfhbx+jxC4ZKkl78br3mrCmwOBEA4HAY9AAAADjE6QM7KiOtuyTp1uk5KgpF/kWkAKClYdADAADgsO49a6B6+eO0rahE981aanUcAMD/YNADAADgsHzRUXp2Yqo8bpc+yMnTrOytVkcCAPwCgx4AAADVSk1uq5tO6ytJuvf9pcor3G9xIgDAQQx6AAAAHNGNo/sqNbmt9paU69Z3clRRwbMeA0AkYNADAADgiKI8bj07MVWtvB7NXbdLL3+/3upIAAAx6AEAAFALvfxxuu/n56N/4rNVWrk9aHEiAACDHgAAALVyyYhknT4gSWXhCk16O1slRtjqSADQojHoAQAAUCsul0uPXTBUiXHRWrl9r57+fJXVkQCgRWPQAwAAoNY6tInR4xcMlSS9+N16zVlbYHEiAGi5GPQAAACokzMGddQlI7rLNKXb3slR0X7D6kgA0CIx6AEAAFBn9541UD0TfcorKtH9s5ZaHQcAWiQGPQAAAOosLiZKz05Mlcft0qzsPH2Qk2d1JABocRj0AAAAqJdh3dvpxtF9JUn3zlyivML9FicCgJaFQQ8AAIB6u/G0vkpJbqtgSblum56jigrT6kgA0GIw6AEAAFBvXo9bz01MVSuvR3PW7tLL36+3OhIAtBhRVgewC8MwZBiRewXXg9kiOSMaho6djX6dj46dryV33C0hWneNO0r3f7BCT/x7lUb2aqujOraxOlaja8kdtxR07Gx26re2GV2maTbqeVFbt25V165dG/NNWiIQCCgQCCgcDis3N1eZmZny+XxWxwIAAIhIpim9sMqtZXvc6uIzdeuQsKI4FxQA6iUUCikjI0NFRUWKj4+v9n6NNui3b9+uRx55RC+99JJCoVBjvMmIEAwGlZCQoIKCgiN+Iq1mGIaysrKUnp4ur9drdRw0ATp2Nvp1Pjp2PjqWCvaV6qy/zdHuYkPXnthDU8b2tzpSo6Jj56NjZ7NTv8FgUH6/v8ZBX6dT7vfs2aPf//73ysrKUnR0tKZMmaIbb7xRDz74oJ566ikNHTpUU6dObXD4SOT1eiO+dMk+OVF/dOxs9Ot8dOx8Lbnjzu28evyCFF3/2kK9PGejzhjUWSP7JFodq9G15I5bCjp2Njv0W9t8dToRasqUKZozZ46uuuoqJSYm6o9//KPOPvts/fjjj/ryyy81b948TZw4sV6BAQAAYH/pgzrqkhHJMk3p1neyVbQ/8h+rCgB2VadB/+mnn2rq1Kl66qmn9OGHH8o0TaWmpuqjjz7S8ccf31QZAQAAYCP3njVIPRN9yisq0QOzllodBwAcq06DPi8vTwMHDpQk9ezZU7GxsbrsssuaJBgAAADsKS4mSs9MTJXH7dL72Xn6MCfP6kgA4Eh1GvSmaSoq6r8Pu/d4PGrVqlWjhwIAAIC9HdO9nf4wuq8k6Z6ZS7StaL/FiQDAeep0UTzTNHX66adXjvr9+/dr/Pjxio6OrnK/H3/8sfESAgAAwJZuOq2v/rNqh3K2FOm26Tl6/Zo0ud0uq2MBgGPUadA/8MADVf58zjnnNGoYAAAAOIfX49azE1N11l++0/drdmnqnA269qReVscCAMdo0KAHAAAAjqR3h9a656yBuvf9pXr8s5U6qa9f/Tu1sToWADhCnR5Dv2PHjiO+vLy8XAsWLGhQIAAAADjLpWndddqAJJWVV2jStGyVloetjgQAjlCnQd+5c+cqo37IkCHavHlz5Z937dqlkSNHNl46AAAA2J7L5dLjFwxVYly0VmwL6pnPc62OBACOUOer3P/Shg0bZBjGEe8DAAAAdGgTo8cuGCpJ+te36zRv3S6LEwGA/dVp0NeGy8WVSwEAAHCo9EEd9evjkmWa0q3v5ChYYtT8SgCAajX6oAcAAACqc9/Zg9Qj0aethfv1wKxlVscBAFur06B3uVzau3evgsGgioqK5HK5tG/fPgWDwcpfAAAAQHXiYqL0zMWpcrukmT9t1UeL86yOBAC2VaenrTNNU0cddVSVPw8bNqzKnznlHgAAAEcyvEc73Ti6r/7y5RrdM3Opju3RXp0SYq2OBQC2U6dB/9VXXzVVDgAAALQgN53eT//J3amcLUW6bXqOXrtmhNxufjAEAHVRp0F/6qmnHvHloVBI2dnZDckDAACAFsDrceuZiak66y/f6rs1BXplzgZdc1Ivq2MBgK006kXxVq9erZNPPrkx3yQAAAAcqk+H1rrnrEGSpMc+W6nc/L0WJwIAe+Eq9wAAALDMZWndNbp/B5WVV2jS29kqLQ9bHQkAbINBDwAAAMu4XC49fuFQtY+L1vJtQT2TlWt1JACwDQY9AAAALJXUJlaPnT9EkvSvb9Zp7tpdFicCAHuo00XxPvjggyO+fP369Q0KAwAAgJbpzKM76dfHJevtHzbr1ney9emkU5TQymt1LACIaHUa9Oeee26N9+F56AEAAFAf9509SPPW7dKGXSHdP2up/u/Xw6yOBAARrU6n3FdUVNT4KxzmQiYAAACou7iYKD07MVUet0uzsvM0K3ur1ZEAIKLV6zH0u3b993FNmzdv1v3336877rhD3377baMFAwAAQMszrHs73XxaP0nSve8v1ZY9IYsTAUDkqtOgX7JkiXr27KmkpCQNGDBA2dnZOu644/Tss8/qn//8p0aPHq3333+/iaICAACgJfjD6D46pntb7S0p163v5ChcYVodCQAiUp0G/R133KEhQ4bom2++0ahRo3T22WfrrLPOUlFRkfbs2aPf/va3euyxx5oqKwAAAFqAKI9bz05MVVy0R/PX79a/vllndSQAiEh1GvQ//PCDHnnkEZ144ol66qmnlJeXp9///vdyu91yu9266aabtHLlyqbKCgAAgBaiR2KcHphwtCTpmaxVWrq1yOJEABB56nSV+927d6tTp06SpNatWysuLk7t2rWrfHm7du20d+/exk0YIQzDkGEYVseo1sFskZwRDUPHzka/zkfHzkfHje/coR31xfIkfb58h25+6ye9f8PxahXtsSwPHTsfHTubnfqtbUaXaZq1flCS2+1Wfn6+OnToIElq06aNFi9erF69ekmS8vPz1aVLF0dc6T4QCCgQCCgcDis3N1eZmZny+XxWxwIAAGhRig3psRyPgoZLJ3eq0IW9KqyOBABNLhQKKSMjQ0VFRYqPj6/2fnUe9OPGjVNMTIwk6cMPP9Rpp52muLg4SVJpaak+++wzRwz6g4LBoBISElRQUHDET6TVDMNQVlaW0tPT5fV6rY6DJkDHzka/zkfHzkfHTefbNQW65tUfJUkvXj5Mpx7VwZIcdOx8dOxsduo3GAzK7/fXOOjrdMr9lVdeWeXPl1122SH3ueKKK+ryJm3D6/VGfOmSfXKi/ujY2ejX+ejY+ei48Z02sLOuPrGnpn6/QVNmLte/J52sxNYxluWhY+ejY2ezQ7+1zVenQT916tR6hQEAAAAa4s6xA/T9mgLl5u/TlPeW6F+XD5fL5bI6FgBYqk5XuQcAAACsEOv16LmJwxTtcStreb7e/mGz1ZEAwHIMegAAANjCoC7xun1Mf0nSwx8u1/qCYosTAYC1GPQAAACwjWtP6qUT+iRqvxHWpGnZMsJc9R5Ay8WgBwAAgG243S49fXGK4mOjlLO5UH/9co3VkQDAMgx6AAAA2ErnhFZ69PwhkqS/fblaizbutjgRAFiDQQ8AAADbOXtoF50/rKsqTGnStGztKy23OhIANDsGPQAAAGzpwXOOVte2rbR593499MEyq+MAQLNj0AMAAMCW4mO9enZiqtwuafqiLfp0yTarIwFAs2LQAwAAwLZG9GqvG0b1kSTdNXOJtheVWJwIAJoPgx4AAAC2dsvpR2lI1wQVhgzdPiNHFRWm1ZEAoFkw6AEAAGBr0VFuPffrVMV63fp2dYFembPB6kgA0CwY9AAAALC9Ph1a696zBkmSHvtspVZuD1qcCACaHoMeAAAAjnBpWnedNiBJZeUVmvR2tkqMsNWRAKBJMegBAADgCC6XS49fMFSJcdFauX2vnv58ldWRAKBJMegBAADgGB3axOiJC4dKkl74dr2+X1NgcSIAaDoMegAAADjK6QM76tK07pKkW9/JUWGozOJEANA0GPQAAABwnHvOGqje/jhtD5bonplLZZo8lR0A52HQAwAAwHF80VF67tepinK79PGSbZr501arIwFAo2PQAwAAwJGGdmurP6YfJUm6f9Yybd4dsjgRADQuBj0AAAAc63en9tGxPdppX2m5/jgtW+EKTr0H4BwMegAAADiWx+3SsxNT1TomSgs37tHz/1lrdSQAaDQMegAAADhacnufHj7naEnSs1m5Wryl0NpAANBIGPQAAABwvPOGddVZQzurvMLUpLezFSortzoSADQYgx4AAACO53K59Mi5g9UpPlbrCor1yMcrrI4EAA3GoAcAAECL0NYXrWcuTpEkvTl/k2avyLc4EQA0DIMeAAAALcYJff26/uRekqQ7ZizWzr2lFicCgPpj0AMAAKBFuW1Mfw3o1Ea7ist057uLZZo8lR0Ae2LQAwAAoEWJifLo/349TNFRbn25cofenL/J6kgAUC8MegAAALQ4/Tu10ZSxAyRJ/+/j5VqzY5/FiQCg7hj0AAAAaJGuOqGnTu7nV4lRoUnTflJZeYXVkQCgThj0AAAAaJHcbpeeuihFbX1eLd0a1P/NzrU6EgDUCYMeAAAALVbH+Fg9dv4QSdLfv16rBet3W5wIAGqPQQ8AAIAWbezgzrpoeDeZpvTHadkKlhhWRwKAWmHQAwAAoMV7YMLR6t7ep62F+/XgrGVWxwGAWmHQAwAAoMVrHROlZyemyO2S3vtpqz7MybM6EgDUiEEPAAAASBreo71uPK2fJOmemUuUV7jf4kQAcGQMegAAAOBnN53WVynJbRUsKdet7+SoosK0OhIAVItBDwAAAPzM63HruYmp8kV7NHfdLr303XqrIwFAtRj0AAAAwC/08sfp/rMHSZKe/PcqLc8LWpwIAA6PQQ8AAAD8j4nHJSt9UEeVhSs0adpPKjHCVkcCgEMw6AEAAID/4XK59Nj5Q+RvHaPc/H16/LOVVkcCgEMw6AEAAIDDSGwdoycvGipJmvr9Bn27psDiRABQFYMeAAAAqMbo/km6cmQPSdKU95Zpn2FxIAD4BccP+s2bN2vUqFEaNGiQhg4dqunTp1sdCQAAADYyZdxA9U1qrR17SzVtnVumyVPZAYgMjh/0UVFReu6557R8+XJ9/vnnmjRpkoqLi62OBQAAAJtoFe3RcxNT5fW4tHi3W+/+lGd1JACQ1AIGfefOnZWamipJ6tSpk/x+v3bv3m1tKAAAANjK4K4JmnR6X0nS//t4pTbu4gdEAKxn+aD/5ptvNH78eHXp0kUul0vvv//+IfcJBALq2bOnYmNjlZaWpgULFtTrfS1atEjhcFjJyckNTA0AAICW5toTe6pvvKnisrD+OC1b5eEKqyMBaOEsH/TFxcVKSUlRIBA47MunTZumyZMn64EHHtCPP/6olJQUjRkzRjt27Ki8T2pqqgYPHnzIr7y8/54OtXv3bl1xxRX617/+1eQfEwAAAJzH43bp0r5htYmN0o+bChX4aq3VkQC0cFFWBxg3bpzGjRtX7cufeeYZXX/99br66qslSc8//7w+/vhjvfzyy5oyZYokKTs7+4jvo7S0VOeee66mTJmiE044ocb7lpaWVv45GAxKkgzDkGFE7mVND2aL5IxoGDp2Nvp1Pjp2Pjp2PsMw1D5Gum/cUbpj5nL95cvVOqF3W6Umt7U6GhoJx7Gz2anf2mZ0mRF0mU6Xy6WZM2fq3HPPlSSVlZXJ5/NpxowZlbdJ0pVXXqnCwkLNmjWrxrdpmqYyMjLUv39/PfjggzXe/8EHH9RDDz10yO2ZmZny+Xy1/VAAAADgYK+tdmtRgVv+WFN3DA0rxmN1IgBOEgqFlJGRoaKiIsXHx1d7P8t/Qn8kBQUFCofD6tixY5XbO3bsqJUrV9bqbXz//feaNm2ahg4dWvn4/Ndff11Dhgw57P3vuusuTZ48ufLPwWBQycnJOvPMM4/4ibSaYRjKyspSenq6vF6v1XHQBOjY2ejX+ejY+ejY+X7Z8UmjpbMDc7WtqEQLwz30yPijrY6HRsBx7Gx26vfgmeI1iehB3xhOOukkVVTU/oIlMTExiomJOeR2r9cb8aVL9smJ+qNjZ6Nf56Nj56Nj5/N6vfL5vHp2YqoueWGe3lm0VacP6qQxR3eyOhoaCcexs9mh39rms/yieEfi9/vl8XiUn59f5fb8/Hx16sT/MAEAAGCd43sn6ren9JEkTXl3sXYESyxOBKCliehBHx0dreHDh2v27NmVt1VUVGj27NkaOXKkhckAAAAAaXL6URrUOV57QoZun7FYEXR5KgAtgOWDft++fcrOzq68Uv369euVnZ2tTZs2SZImT56sF154Qa+++qpWrFihG264QcXFxZVXvQcAAACsEh3l1v/9OlUxUW79J3enXpu70epIAFoQyx9Dv3DhQo0ePbryzwcvSHfllVfqlVde0cSJE7Vz507df//92r59u1JTU/XZZ58dcqE8AAAAwAr9OrbR3b8aqAc+WKZHP1mhE/okql/HNlbHAtACWD7oR40aVeOpSTfeeKNuvPHGZkp0eDwPPaxGx85Gv85Hx85Hx853pI4vObaLZq/Yrm9W79LNb/2k6b9NU0yU5SfDoo44jp3NTv3a8nnoI0kgEFAgEFA4HFZubi7PQw8AAIAjCpZJj+V4VFzu0uldKjShR+2faQkAfqm2z0PPoK9BMBhUQkKCCgoKeB56WIqOnY1+nY+OnY+Ona82HX+xYoduyMyWyyW9fvWxSuvVvplToiE4jp3NTv0Gg0H5/f4aB73lp9zbhR2eq1CyT07UHx07G/06Hx07Hx0735E6Hje0qy5Zs0tvLdisO95dqk8nnaKEVvx9sBuOY2ezQ7+OeB56AAAAwG7uPWuQeib6lFdUovveX2p1HAAOxqAHAAAAGlFcTJSenZgqj9ulD3LyNCt7q9WRADgUgx4AAABoZMO6t9PNp/WTJN37/lJt2ROyOBEAJ2LQAwAAAE3gD6P76JjubbW3pFyT38lRuIJrUQNoXAx6AAAAoAlEedx6dmKq4qI9WrB+t/71zTqrIwFwGK5yX0uGYcgwDKtjVOtgtkjOiIahY2ejX+ejY+ejY+erT8dd4qN171kDdNfMZXoma5VG9mqro7tE7lMht3Qcx85mp35rm5Hnoa9GIBBQIBBQOBxWbm6uMjMz5fP5rI4FAAAAmzFNaWquWzm73erYytRtQ8KK9lidCkAkC4VCysjIqPF56Bn0NQgGg0pISFBBQcERP5FWMwxDWVlZSk9Pj/jnVET90LGz0a/z0bHz0bHzNaTjPaEynf23udqxt1SXpSXrgbMHNlFKNATHsbPZqd9gMCi/31/joOeU+1ryer0RX7pkn5yoPzp2Nvp1Pjp2Pjp2vvp0nJTg1VMXpeiKlxfojfmbdfrATho9IKmJEqKhOI6dzQ791jYfF8UDAAAAmsEpR3XQ1Sf2lCTdPmOxdu0rtTYQANtj0AMAAADN5M6xA3RUx9Yq2FeqO99dIh79CqAhGPQAAABAM4n1evTcxGGK9rj1xYp8vf3DZqsjAbAxBj0AAADQjAZ1idftY/pLkh7+cLnWFxRbnAiAXTHoAQAAgGZ27Um9dEKfRO03wpo0LVtGuMLqSABsiEEPAAAANDO326WnL05RfGyUcjYX6q+zV1sdCYAN8bR1tWQYhgzDsDpGtQ5mi+SMaBg6djb6dT46dj46dr7G7tjvi9KfJgzSLe8s1t++WqMT+7TXMd3bNsrbRv1wHDubnfqtbUaXyaU1DysQCCgQCCgcDis3N1eZmZny+XxWxwIAAIDDvLHarR8K3EqMMXVHSlixHqsTAbBaKBRSRkaGioqKFB8fX+39GPQ1CAaDSkhIUEFBwRE/kVYzDENZWVlKT0+X1+u1Og6aAB07G/06Hx07Hx07X1N1vLekXBMCc7SlsETnD+uix88f3GhvG3XDcexsduo3GAzK7/fXOOg55b6WvF5vxJcu2Scn6o+OnY1+nY+OnY+Ona+xO27v9eqZicP063/N1Xs/5emMQZ30qyGdG+3to+44jp3NDv3WNh8XxQMAAAAsNqJXe90wqo8k6e6ZS7S9qMTiRADsgEEPAAAARIBJZxylod0SVBgydNv0HFVU8MhYAEfGoAcAAAAigNfj1rMTUxXrdeu7NQWaOmeD1ZEARDgGPQAAABAh+nRorXvPGiRJevyzlVq5PWhxIgCRjEEPAAAARJBL07rr9AFJKiuv0KS3s1VihK2OBCBCMegBAACACOJyufT4hUPlbx2tldv36ql/r7I6EoAIxaAHAAAAIoy/dYwev2CoJOnF79br+zUFFicCEIkY9AAAAEAEOn1gR12a1l2SdOs7OSoMlVmcCECkibI6gF0YhiHDMKyOUa2D2SI5IxqGjp2Nfp2Pjp2Pjp3Pio7vPLOf5q4t0LqCkO56d7H+b+JQuVyuZnv/LQ3HsbPZqd/aZnSZpskTXB5GIBBQIBBQOBxWbm6uMjMz5fP5rI4FAACAFmbTPunZpR5VmC5d2jesER348h1wulAopIyMDBUVFSk+Pr7a+zHoaxAMBpWQkKCCgoIjfiKtZhiGsrKylJ6eLq/Xa3UcNAE6djb6dT46dj46dj4rO/7Hf9bpmS/WKC7Gow//MFLJ7fhBU1PgOHY2O/UbDAbl9/trHPSccl9LXq834kuX7JMT9UfHzka/zkfHzkfHzmdFx3847Sh9u2aXftiwR3e8u0zTfjtSHjen3jcVjmNns0O/tc3HRfEAAACACOdxu/TMxalqHROlhRv36B9fr7E6EoAIwKAHAAAAbCC5vU8Pn3O0JOm5L1YrZ3OhtYEAWI5BDwAAANjEecO66qyhnVVeYeqP07IVKiu3OhIACzHoAQAAAJtwuVx69Nwh6pwQq3UFxfp/H6+wOhIACzHoAQAAABtJ8Hn19EUpkqTM+Zv0xfJ8ixMBsAqDHgAAALCZE/r6df3JvSRJd767WDv3llqcCIAVGPQAAACADd02pr8GdGqjXcVluvPdxTJN0+pIAJoZgx4AAACwoZgoj/7v18MUHeXWlyt36I35m6yOBKCZMegBAAAAm+rfqY2mjB0gSXrk4+Vas2OfxYkANKcoqwPYhWEYMgzD6hjVOpgtkjOiYejY2ejX+ejY+ejY+SK140uP66rZK/L1/dpduuXtH/XO9WmKjuLndvURqR2jcdip39pmdJk82OawAoGAAoGAwuGwcnNzlZmZKZ/PZ3UsAAAA4BBFZdJjOR6Fyl06o2uFxnevsDoSgAYIhULKyMhQUVGR4uPjq70fg74GwWBQCQkJKigoOOIn0mqGYSgrK0vp6enyer1Wx0EToGNno1/no2Pno2Pni/SO/70sXze+nSOXS3rzmuN0XM92VkeynUjvGA1jp36DwaD8fn+Ng55T7mvJ6/VGfOmSfXKi/ujY2ejX+ejY+ejY+SK147NTu+k/q3dp+qItuv3dpfp00smKj428nHYQqR2jcdih39rm48E1AAAAgEM8MOFodW/v09bC/Xpw1jKr4wBoYgx6AAAAwCFax0Tp2Ympcruk937aqg9z8qyOBKAJMegBAAAABxneo51uPK2fJOmemUuUV7jf4kQAmgqDHgAAAHCYm07rq5TktgqWlOvWd3JUUcF1sAEnYtADAAAADuP1uPXcxFT5oj2au26XXvxundWRADQBBj0AAADgQL38cbr/7EGSpCf/vUrL84IWJwLQ2Bj0AAAAgENNPC5Z6YM6ygibmjTtJ5UYYasjAWhEDHoAAADAoVwulx47f4g6tIlRbv4+Pf7ZSqsjAWhEDHoAAADAwRJbx+jJC4dKkqZ+v0Hf5O60OBGAxsKgBwAAABxuVP8kXTmyhyTp1uk52l1cZnEiAI2BQQ8AAAC0AHf9aqD6JrXWzr2luvu9JTJNnsoOsDsGPQAAANACxHo9em5iqrwelz5btl3TF26xOhKABmLQAwAAAC3E4K4JuvXM/pKkBz9cpo27ii1OBKAhoqwOYBeGYcgwDKtjVOtgtkjOiIahY2ejX+ejY+ejY+dzSsdXHZ+sL1fka8GGPbrl7Z/01rXHKcrDz/kk53SMw7NTv7XN6DJ58MxhBQIBBQIBhcNh5ebmKjMzUz6fz+pYAAAAQIPtLpWeyPFof9ilcd3CGpvMJAAiSSgUUkZGhoqKihQfH1/t/Rj0NQgGg0pISFBBQcERP5FWMwxDWVlZSk9Pl9frtToOmgAdOxv9Oh8dOx8dO5/TOv5w8TZNnr5EHrdLb193nFKT21odyXJO6xhV2anfYDAov99f46DnlPta8nq9EV+6ZJ+cqD86djb6dT46dj46dj6ndHz+8O76z+pdmpWdpztnLtMnN5+sWK/H6lgRwSkd4/Ds0G9t8/FgGQAAAKCFenjCYHWMj9G6ncV68t+rrI4DoI4Y9AAAAEALleDz6rELhkqSXv5+vRas321xIgB1waAHAAAAWrDR/ZM08dhkmaZ02/QcFZeWWx0JQC0x6AEAAIAW7t6zB6pLQqw27Q7p8c9WWh0HQC0x6AEAAIAWrk2sV09cmCJJem3uRn2/psDiRABqg0EPAAAAQCf18+uy47tLku6YsVh7SwyLEwGoCYMeAAAAgCTprnED1b29T1sL9+uRj1dYHQdADRj0AAAAACRJcTFRevLCoXK5pLd/2KyvVu2wOhKAI2DQAwAAAKiU1jtRV5/QS5I05d3FKgpx6j0QqRj0AAAAAKq4Y2x/9fbHKT9Yqoc+XGZ1HADVYNADAAAAqCLW69FTF6fI7ZLe+2mrPl+23epIAA6DQQ8AAADgEMd0b6ffnNJHknT3zCXaXVxmcSIA/4tBDwAAAOCw/pjeT0d1bK2CfWW6f9ZSq+MA+B8MegAAAACHFRPl0dMXpcrjdumjxdv08eJtVkcC8AsMegAAAADVGtItQX8YdeDU+3vfX6Kde0stTgTgIAY9AAAAgCO68bR+GtQ5XntChu6ZuUSmaVodCYAY9AAAAABqEB3l1tMXp8jrcenz5fl6P3ur1ZEAiEEPAAAAoBYGdo7XLaf3kyQ9MGuZtheVWJwIQJTVAezCMAwZhmF1jGodzBbJGdEwdOxs9Ot8dOx8dOx8dCxde0J3fb5suxZvDerOGTl64fJhcrlcVsdqNHTsbHbqt7YZXSYPgDmsQCCgQCCgcDis3NxcZWZmyufzWR0LAAAAsNT2kPTkYo/KTZd+3TuskR2ZE0BjC4VCysjIUFFRkeLj46u9H4O+BsFgUAkJCSooKDjiJ9JqhmEoKytL6enp8nq9VsdBE6BjZ6Nf56Nj56Nj56Pj/3rxuw16/N+5iovx6OMbT1DXtq2sjtQo6NjZ7NRvMBiU3++vcdBzyn0teb3eiC9dsk9O1B8dOxv9Oh8dOx8dOx8dS785ta++WLlTizbu0T2zluuNa9Mcdeo9HTubHfqtbT4uigcAAACgTjxul566KEWxXre+X7NLb8zfZHUkoEVi0AMAAACos17+ON05doAk6c+frNCmXSGLEwEtD4MeAAAAQL1cObKnju/dXqGysG6bkaOKCi7PBTQnBj0AAACAenG7XXrywhTFRXu0YP1uTZ2zwepIQIvCoAcAAABQb8ntfbr7rIGSpCc+W6m1O/dZnAhoORj0AAAAABokY0R3ndzPr9LyCt02PUdhTr0HmgWDHgAAAECDuFwuPX7BULWJidJPmwr1r2/WWR0JaBEY9AAAAAAarEvbVrp//CBJ0rNZuVq1fa/FiQDnY9ADAAAAaBQXDu+m0wckqSx84NR7I1xhdSTA0Rj0AAAAABqFy+XSn88fooRWXi3ZWqR/fL3W6kiAozHoAQAAADSapPhYPXzO0ZKkv8xerWV5RRYnApyLQQ8AAACgUU1I6aKxR3dSeYWpW9/JUVk5p94DTYFBDwAAAKBRuVwu/b/zBqt9XLRWbt+rv8xebXUkwJEY9AAAAAAanb91jB45d7Ak6R//WauczYXWBgIciEEPAAAAoEmMG9JZE1K6KFxh6tbpOSoxwlZHAhyFQQ8AAACgyTw04Wh1aBOjNTv26ZmsXKvjAI7CoAcAAADQZNrFRevP5w2RJL3w7Tot3LDb4kSAczDoAQAAADSpMwZ11AXHdJNpSrdNz9H+Mk69BxoDgx4AAABAk7t//CB1io/Vhl0hPf7ZSqvjAI7AoAcAAADQ5BJaefX4hUMlSa/M2aC5a3dZnAiwPwY9AAAAgGZx6lEddMmI7pKk22fkaF9pucWJAHtj0AMAAABoNvecNVDd2rXSlj379egnK6yOA9gagx4AAABAs2kdE6Unfj71PnP+Jn2Tu9PiRIB9MegBAAAANKsT+vh11Qk9JUl3vrtYRfsNawMBNsWgBwAAANDs7hjbXz0TfdpWVKI/fbTc6jiALTHoAQAAADQ7X3SUnrooRS6XNGPRFs1ekW91JMB2GPQAAAAALHFsz/a67qRekqQp7y3RnuIyixMB9hJldQC7MAxDhhG5j+05mC2SM6Jh6NjZ6Nf56Nj56Nj56Lhp3Dy6t2av2KF1BcW6f9YSPXPRUMuy0LGz2anf2mZ0maZpNnEWWwoEAgoEAgqHw8rNzVVmZqZ8Pp/VsQAAAADH2bhXenapR6ZcuuaosFISmSho2UKhkDIyMlRUVKT4+Phq78egr0EwGFRCQoIKCgqO+Im0mmEYysrKUnp6urxer9Vx0ATo2Nno1/no2Pno2PnouGk9k7Va//hmvdrHefXJTScqMS662TPQsbPZqd9gMCi/31/joOeU+1ryer0RX7pkn5yoPzp2Nvp1Pjp2Pjp2PjpuGpPO7K+vcgu0cvtePfTRSv390mPkcrksyULHzmaHfmubj4viAQAAALBcTJRHT1+coii3S58u3a4PcvKsjgREPAY9AAAAgIhwdJcE3XRaP0nS/bOWaUewxOJEQGRj0AMAAACIGL8f3UeDu8araL+hu95bIi75BVSPQQ8AAAAgYng9bj19UaqiPW7NXrlDMxZtsToSELEY9AAAAAAiSv9ObTQp/cCp9w9/uFx5hfstTgREJgY9AAAAgIjzm5N7KzW5rfaWluvOdxdz6j1wGAx6AAAAABEnyuPW0xenKCbKrW9XF+itBZutjgREHAY9AAAAgIjUp0Nr3T6mvyTpkY+Xa/PukMWJgMjCoAcAAAAQsa45sZdG9Gyv4rKwbp+Ro4oKTr0HDmLQAwAAAIhYbrdLT140VK28Hs1bt1uvzd1gdSQgYjDoAQAAAES0HolxuvtXAyRJj322UusLii1OBEQGBj0AAACAiHdpWg+d0CdRJUaFbpueozCn3gMMegAAAACRz+126YkLh6p1TJQWbdyjl75bZ3UkwHIMegAAAAC20K2dT/eeNVCS9NTnuVqdv9fiRIC1GPQAAAAAbGPiccka1b+DysoPnHpfHq6wOhJgGQY9AAAAANtwuVx67Pyhio+NUs6WIv3zG069R8vFoAcAAABgK50SYvXghKMlSc99kasV24IWJwKswaAHAAAAYDvnDeuq9EEdZYRN3fpOjsrKOfUeLQ+DHgAAAIDtuFwuPXreELXzebV8W1B/+2qN1ZGAZsegBwAAAGBLHdrE6OFzBkuSAl+t0ZItRRYnApoXgx4AAACAbY1P6aKzhnRWuMLUrdOzVVoetjoS0GwY9AAAAABs7U/nDpa/dbRy8/fp2azVVscBmg2DHgAAAICttY+L1iPnDZEk/eubtfpx0x6LEwHNg0EPAAAAwPbGHN1J5w3rqgpTuu2dHJUYnHoP52PQAwAAAHCEB8cfrY7xMVpXUKwn/73K6jhAk2PQAwAAAHCEBJ9Xj10wVJL08vfrtWD9bosTAU2LQQ8AAADAMUb3T9LEY5NlmtJt03NUXFpudSSgyTDoAQAAADjKvWcPVJeEWG3aHdJjn660Og7QZBj0AAAAABylTaxXT1yYIkl6fd5Gfb+mwOJEQNNg0AMAAABwnJP6+XXZ8d0lSXfMWKy9JYbFiYDGx6AHAAAA4Eh3jRuo7u192lq4X//voxVWxwEaHYMeAAAAgCPFxUTpyQuHyuWSpi3crK9W7rA6EtCoGPQAAAAAHCutd6KuPqGXJGnKe4tVFOLUezgHgx4AAACAo90xtr96++OUHyzVQx8uszoO0GgY9AAAAAAcLdbr0VMXp8jtkt77aas+X7bd6khAo2DQAwAAAHC8Y7q3029O6SNJunvmEu0uLrM4EdBwDHoAAAAALcKkM/qpX1JrFewr032zllodB2gwBj0AAACAFiHW69EzF6fK43bp48Xb9NHiPKsjAQ3CoAcAAADQYgzplqA/jDpw6v197y/Vzr2lFicC6o9BDwAAAKBFufG0fhrUOV57QobunrlEpmlaHQmoFwY9AAAAgBYlOsqtpy9OkdfjUtbyfM38aavVkYB6YdADAAAAaHEGdo7XLaf3kyQ9+MEybS8qsTgRUHcMegAAAAAt0u9O7aOUbgkKlpRrynuLOfUetsOgBwAAANAiRXkOnHofHeXW16t2asaPnHoPe3H8oC8sLNSxxx6r1NRUDR48WC+88ILVkQAAAABEiL5JbXTbmUdJkh75dJV2c9F72EiU1QGaWps2bfTNN9/I5/OpuLhYgwcP1vnnn6/ExESrowEAAACIANee1Fv/XpavRRv36K21bmVUcOo97MHxP6H3eDzy+XySpNLSUpmmyWNjAAAAAFTyuF166qIUxXrdyi1y660fNlsdCagVywf9N998o/Hjx6tLly5yuVx6//33D7lPIBBQz549FRsbq7S0NC1YsKBO76OwsFApKSnq1q2bbr/9dvn9/kZKDwAAAMAJevnjdFv6gaveP/7vXG3cVWxxIqBmlg/64uJipaSkKBAIHPbl06ZN0+TJk/XAAw/oxx9/VEpKisaMGaMdO3ZU3ufg4+P/91deXp4kqW3btsrJydH69euVmZmp/Pz8ZvnYAAAAANjH5Wnd1Te+QvuNCt0+fbEqOPUeEc7yx9CPGzdO48aNq/blzzzzjK6//npdffXVkqTnn39eH3/8sV5++WVNmTJFkpSdnV2r99WxY0elpKTo22+/1YUXXnjY+5SWlqq09L9XwggGg5IkwzBkGEat3o8VDmaL5IxoGDp2Nvp1Pjp2Pjp2Pjp2vnC4XBl9KvTUMq8WbNitF79dq6tP6GF1LDQSOx3Dtc3oMiPoAeUul0szZ87UueeeK0kqKyuTz+fTjBkzKm+TpCuvvFKFhYWaNWtWjW8zPz9fPp9Pbdq0UVFRkU488US99dZbGjJkyGHv/+CDD+qhhx465PbMzMzKx+IDAAAAcK7v8116Z51HXpep21PC6tjK6kRoaUKhkDIyMlRUVKT4+Phq72f5T+iPpKCgQOFwWB07dqxye8eOHbVy5cpavY2NGzfqN7/5TeXF8G666aZqx7wk3XXXXZo8eXLln4PBoJKTk3XmmWce8RNpNcMwlJWVpfT0dHm9XqvjoAnQsbPRr/PRsfPRsfPRsfMd7PiBS09X3ltL9N2aXfpkV6Levm6EPG6X1fHQQHY6hg+eKV6TiB70jWHEiBG1PiVfkmJiYhQTE3PI7V6vN+JLl+yTE/VHx85Gv85Hx85Hx85Hx84XHR2tJy5M0Zhnv1H25iJNnbtZN4zqY3UsNBI7HMO1zWf5RfGOxO/3y+PxHHIRu/z8fHXq1MmiVAAAAACcrkvbVrpv/CBJ0rNZuVq1fa/FiYBDRfSgj46O1vDhwzV79uzK2yoqKjR79myNHDnSwmQAAAAAnO6i4d10+oAklYUrdOv0bBnhCqsjAVVYPuj37dun7OzsytPi169fr+zsbG3atEmSNHnyZL3wwgt69dVXtWLFCt1www0qLi6uvOo9AAAAADQFl8ulP58/RAmtvFq6Nai/f7XW6khAFZY/hn7hwoUaPXp05Z8PXpDuyiuv1CuvvKKJEydq586duv/++7V9+3alpqbqs88+O+RCeQAAAADQ2JLiY/XwOUfrlrez9dcvV+v0gUka3DXB6liApAgY9KNGjVJNz5x344036sYbb2ymRIfH89DDanTsbPTrfHTsfHTsfHTsfNV1PG5QB308KEmfL9+hW9/J1ru/O14xUZaf7Iw6stMxbMvnoY8kgUBAgUBA4XBYubm5PA89AAAA0ILtNaQ/Z3tUXO5SetcKnd2dx9Oj6dT2eegZ9DUIBoNKSEhQQUEBz0MPS9Gxs9Gv89Gx89Gx89Gx89XU8WfL8nXT2zlyu6Tpv0nT0G6cem8ndjqGg8Gg/H5/jYPe8lPu7cIOz1Uo2Scn6o+OnY1+nY+OnY+OnY+Ona+6jsendlPWip36ICdPd85cpo9uOkmxXo8FCdEQdjiGHfE89AAAAAAQSR6acLQ6tInRmh379ExWrtVx0MIx6AEAAACgltrFRevP5w2RJL3w7Tot3LDb4kRoyRj0AAAAAFAHZwzqqAuO6SbTlG6bnqNQWbnVkdBCMegBAAAAoI7uHz9IneJjtWFXSE98tsrqOGihGPQAAAAAUEcJrbx6/MKhkqRX5mzQnLUFFidCS8RV7mvJMAwZhmF1jGodzBbJGdEwdOxs9Ot8dOx8dOx8dOx8de34hF5tNfHYbpq2cIvumJ6jD288Qa1jmFiRyk7HcG0z8jz01QgEAgoEAgqHw8rNzVVmZqZ8Pp/VsQAAAABEkJKw9HiOR7tLXTohqUIT+1RYHQkOEAqFlJGRUePz0DPoaxAMBpWQkKCCgoIjfiKtZhiGsrKylJ6eHvHPqYj6oWNno1/no2Pno2Pno2Pnq2/H89bt1uVTF0qSXr7iGJ3cz99UEdEAdjqGg8Gg/H5/jYOe80Fqyev1Rnzpkn1yov7o2Nno1/no2Pno2Pno2Pnq2vHJ/TvqqhN66pU5G3T3+8v17z+eooRW/B2JVHY4hmubj4viAQAAAEAD3TG2v3om+rQ9WKI/fbTc6jhoIRj0AAAAANBAvugoPXVRilwuacaiLZq9It/qSGgBGPQAAAAA0AiO7dle153US5I05b0l2lNcZnEiOB2DHgAAAAAaya1n9lefDnHaubdUD3ywzOo4cDgGPQAAAAA0klivR09fnCq3S/ogJ0+fLtlmdSQ4GIMeAAAAABpRanJb3TCqjyTpnveXqmBfqcWJ4FQ8bV0tGYYhwzCsjlGtg9kiOSMaho6djX6dj46dj46dj46drzE7vuGUXvpieb5W5e/T3e8t1t9+nSKXy9Xgt4v6s9MxXNuMLtM0zSbOYkuBQECBQEDhcFi5ubnKzMyUz+ezOhYAAAAAm9hSLD29xKMK06XL+4Z1bAemF2onFAopIyNDRUVFio+Pr/Z+DPoaBINBJSQkqKCg4IifSKsZhqGsrCylp6fL6/VaHQdNgI6djX6dj46dj46dj46dryk6/utXa/WXL9cqoVWUPrnpRCW1iWmUt4u6s9MxHAwG5ff7axz0nHJfS16vN+JLl+yTE/VHx85Gv85Hx85Hx85Hx87XmB3fdPpR+nLVTi3dGtT9H6zQi1cey6n3FrPDMVzbfFwUDwAAAACaiNfj1tMXpSra49bslTs0Y9EWqyPBQRj0AAAAANCE+ndqo0np/SRJD3+4XHmF+y1OBKdg0AMAAABAE/vNyb2VmtxWe0vLdee7i8WlzNAYGPQAAAAA0MSiPG49fXGKYqLc+nZ1gTIXbLI6EhyAQQ8AAAAAzaBPh9a6fUx/SdIjH6/QVk69RwMx6AEAAACgmVxzYi8N7hqvUFlY/1m10+o4sDkGPQAAAAA0E7fbpe7tfZKkcEWFxWlgdzwPfS0ZhiHDMKyOUa2D2SI5IxqGjp2Nfp2Pjp2Pjp2Pjp2vuTquqDhwQbxwOMzfp2Zkp2O4thldJpdXPKxAIKBAIKBwOKzc3FxlZmbK5/NZHQsAAACAzU1d5Vb2brcu7BXWyZ2YYzhUKBRSRkaGioqKFB8fX+39GPQ1CAaDSkhIUEFBwRE/kVYzDENZWVlKT0+X1+u1Og6aAB07G/06Hx07Hx07Hx07X3N1fNPbOfpsWb4ePHuALk3r3mTvB1XZ6RgOBoPy+/01DnpOua8lr9cb8aVL9smJ+qNjZ6Nf56Nj56Nj56Nj52vqjt1ulyTJ4/Hwd8kCdjiGa5uPi+IBAAAAAGBDDHoAAAAAsACPfUZDMegBAAAAoBm55LI6AhyCQQ8AAAAAgA0x6AEAAAAAsCEGPQAAAAAANsSgBwAAAADAhhj0AAAAAADYEIMeAAAAACxg8rx1aCAGPQAAAAA0J561Do0kyuoAdmEYhgzDsDpGtQ5mi+SMaBg6djb6dT46dj46dj46dr7m6tisqJAkhcNh/j41Izsdw7XN6DJNTvQ4nEAgoEAgoHA4rNzcXGVmZsrn81kdCwAAAIDNvZLr1k+73LqgZ1indGaO4VChUEgZGRkqKipSfHx8tfdj0NcgGAwqISFBBQUFR/xEWs0wDGVlZSk9PV1er9fqOGgCdOxs9Ot8dOx8dOx8dOx8zdXxLdNy9MnSfN1/1gBdfnz3Jns/qMpOx3AwGJTf769x0HPKfS15vd6IL12yT07UHx07G/06Hx07Hx07Hx07X1N37HIfuJSZx+Ph75IF7HAM1zYfF8UDAAAAAMCGGPQAAAAAYAEe/YyGYtADAAAAQDPiWevQWBj0AAAAAADYEIMeAAAAAAAbYtADAAAAAGBDDHoAAAAAAGyI56GvwcErTwaDQYuTHJlhGAqFQgoGgxH/nIqoHzp2Nvp1Pjp2Pjp2Pjp2vubquCy0TxWlIYWK90b8znASOx3DB/9e1PRMCC6T50o4oi1btig5OdnqGAAAAACAFmbz5s3q1q1btS9n0NegoqJCeXl5atOmjVyuyH2CiWAwqOTkZG3evFnx8fFWx0EToGNno1/no2Pno2Pno2Pno2Nns1O/pmlq79696tKli9zu6h8pzyn3NXC73Uf8jkikiY+Pj/i/nGgYOnY2+nU+OnY+OnY+OnY+OnY2u/SbkJBQ4324KB4AAAAAADbEoAcAAAAAwIYY9A4RExOjBx54QDExMVZHQROhY2ejX+ejY+ejY+ejY+ejY2dzYr9cFA8AAAAAABviJ/QAAAAAANgQgx4AAAAAABti0AMAAAAAYEMMegAAAAAAbIhBDwAAAACADTHobeDrr7+Wy+U67K8ffvih2tcrKSnRH/7wByUmJqp169a64IILlJ+fX+U+mzZt0llnnSWfz6ekpCTdfvvtKi8vb+oPCdX4+OOPlZaWplatWqldu3Y699xzj3j/6v5ePPnkk5X36dmz5yEvf+yxx5r4I0F16trxVVdddUh/Y8eOrXKf3bt369JLL1V8fLzatm2ra6+9Vvv27WvCjwLVqUu/hmHozjvv1JAhQxQXF6cuXbroiiuuUF5eXpX7cQxHlroew6Zp6v7771fnzp3VqlUrnXHGGVq9enWV+3AMR466Hm8bNmyo9t/i6dOnV97vcC9/++23m+NDwv+oz/9TR40adcjr/O53v6tyH76mjhx17Xj37t266aab1L9/f7Vq1Urdu3fXzTffrKKioir3i9TjOMrqAKjZCSecoG3btlW57b777tPs2bN17LHHVvt6f/zjH/Xxxx9r+vTpSkhI0I033qjzzz9f33//vSQpHA7rrLPOUqdOnTRnzhxt27ZNV1xxhbxerx599NEm/ZhwqHfffVfXX3+9Hn30UZ122mkqLy/X0qVLj/g6//v34tNPP9W1116rCy64oMrtDz/8sK6//vrKP7dp06bxgqPW6tOxJI0dO1ZTp06t/PP/PnfqpZdeqm3btikrK0uGYejqq6/Wb37zG2VmZjb6x4Dq1bXfUCikH3/8Uffdd59SUlK0Z88e3XLLLZowYYIWLlxY5b4cw5GhPsfwE088ob/85S969dVX1atXL913330aM2aMli9frtjYWEkcw5GmLsdbcnLyIf8W/+tf/9KTTz6pcePGVbl96tSpVb4h27Zt28YJjDqrz/9Tr7/+ej388MOVf/b5fJW/52vqyFOXjvPy8pSXl6ennnpKgwYN0saNG/W73/1OeXl5mjFjRpX7RuRxbMJ2ysrKzA4dOpgPP/xwtfcpLCw0vV6vOX369MrbVqxYYUoy586da5qmaX7yySem2+02t2/fXnmff/zjH2Z8fLxZWlradB8ADmEYhtm1a1fzxRdfbNDbOeecc8zTTjutym09evQwn3322Qa9XTRcfTu+8sorzXPOOafaly9fvtyUZP7www+Vt3366aemy+Uyt27dWt+4qKPGOoYXLFhgSjI3btxYeRvHcGSoT8cVFRVmp06dzCeffLLytsLCQjMmJsZ86623TNPkGI40jXG8paammtdcc02V2ySZM2fObNDbReOoT8ennnqqecstt1T7cr6mjiyNcRy/8847ZnR0tGkYRuVtkXocc8q9DX3wwQfatWuXrr766mrvs2jRIhmGoTPOOKPytgEDBqh79+6aO3euJGnu3LkaMmSIOnbsWHmfMWPGKBgMatmyZU33AeAQP/74o7Zu3Sq3261hw4apc+fOGjduXK1+entQfn6+Pv74Y1177bWHvOyxxx5TYmKihg0bpieffJJTwCzQkI6//vprJSUlqX///rrhhhu0a9euypfNnTtXbdu2rXK2zhlnnCG326358+c3yceCQzXGMSxJRUVFcrlch3zHn2PYevXpeP369dq+fXuVf4sTEhKUlpZW5d9ijuHI0pDjbdGiRcrOzj7sv8V/+MMf5Pf7NWLECL388ssyTbMxY6MO6tPxm2++Kb/fr8GDB+uuu+5SKBSqfBlfU0eehv67WVRUpPj4eEVFVT2hPRKPY065t6GXXnpJY8aMUbdu3aq9z/bt2xUdHX3IF4UdO3bU9u3bK+/zy//xHHz5wZeh+axbt06S9OCDD+qZZ55Rz5499fTTT2vUqFHKzc1V+/bta3wbr776qtq0aaPzzz+/yu0333yzjjnmGLVv315z5szRXXfdpW3btumZZ55pko8Fh1ffjseOHavzzz9fvXr10tq1a3X33Xdr3Lhxmjt3rjwej7Zv366kpKQqrxMVFaX27dtzHDejxjiGS0pKdOedd+qSSy5RfHx85e0cw5GhPh0fPAYP92/tL/8t5hiOHA093l566SUNHDhQJ5xwQpXbH374YZ122mny+Xz6/PPP9fvf/1779u3TzTff3BQfBo6gPh1nZGSoR48e6tKlixYvXqw777xTq1at0nvvvSeJr6kjTUOP44KCAv3pT3/Sb37zmyq3R+xxbPUpAi3ZnXfeaUo64q8VK1ZUeZ3NmzebbrfbnDFjxhHf9ptvvmlGR0cfcvtxxx1n3nHHHaZpmub1119vnnnmmVVeXlxcbEoyP/nkkwZ+dDDN2nf85ptvmpLMf/7zn5WvW1JSYvr9fvP555+v1fvq37+/eeONN9Z4v5deesmMiooyS0pK6v1x4b+as2PTNM21a9eakswvvvjCNE3TfOSRR8yjjjrqkPt16NDB/Pvf/97wD7CFa65+y8rKzPHjx5vDhg0zi4qKjnhfjuHG1ZQdf//996YkMy8vr8rtF110kXnxxRebpskx3Bzq8/XWQXU53kKhkJmQkGA+9dRTNd73vvvuM7t161bnjwWH11wdHzR79mxTkrlmzRrTNPmaujk0V8dFRUXmiBEjzLFjx5plZWVHvG+kHMf8hN5Ct956q6666qoj3qd3795V/jx16lQlJiZqwoQJR3y9Tp06qaysTIWFhVV+Sp+fn69OnTpV3mfBggVVXu/gVfAP3gcNU9uOD15QZ9CgQZW3x8TEqHfv3tq0aVON7+fbb7/VqlWrNG3atBrvm5aWpvLycm3YsEH9+/ev8f44subq+Jdvy+/3a82aNTr99NPVqVMn7dixo8p9ysvLtXv3bo7jRtAc/RqGoYsvvlgbN27Ul19+WeWn84fDMdy4mrLjg8dgfn6+OnfuXHl7fn6+UlNTK+/DMdy06vP11kF1Od5mzJihUCikK664osZMaWlp+tOf/qTS0tJDLnSKumuujn/5OpK0Zs0a9enTh6+pm0FzdLx3716NHTtWbdq00cyZM+X1eo/4/iLlOGbQW6hDhw7q0KFDre9vmqamTp1aedXMIxk+fLi8Xq9mz55decXzVatWadOmTRo5cqQkaeTIkXrkkUe0Y8eOytP9srKyFB8fX+ULFtRfbTsePny4YmJitGrVKp100kmSDnyRv2HDBvXo0aPG13/ppZc0fPhwpaSk1Hjf7Oxsud3uQ07xRP00V8cHbdmyRbt27aocByNHjlRhYaEWLVqk4cOHS5K+/PJLVVRUVH7Bgfpr6n4PjvnVq1frq6++UmJiYo3vi2O4cTVlx7169VKnTp00e/bsygEfDAY1f/583XDDDZI4hptDXb/e+qW6HG8vvfSSJkyYUKv3lZ2drXbt2jHmG0lzdfzL15FU5d9ivqZuWk3dcTAY1JgxYxQTE6MPPvig8llIanq7EXEcW32KAGrviy++qPZ0ki1btpj9+/c358+fX3nb7373O7N79+7ml19+aS5cuNAcOXKkOXLkyMqXl5eXm4MHDzbPPPNMMzs72/zss8/MDh06mHfddVezfDyo6pZbbjG7du1q/vvf/zZXrlxpXnvttWZSUpK5e/fuyvv079/ffO+996q8XlFRkenz+cx//OMfh7zNOXPmmM8++6yZnZ1trl271nzjjTfMDh06mFdccUWTfzw4VF073rt3r3nbbbeZc+fONdevX29+8cUX5jHHHGP269evymljY8eONYcNG2bOnz/f/O6778x+/fqZl1xySbN/fC1dXfstKyszJ0yYYHbr1s3Mzs42t23bVvnr4FWROYYjS33+P/3YY4+Zbdu2NWfNmmUuXrzYPOecc8xevXqZ+/fvr7wPx3BkqM3xdrivt0zTNFevXm26XC7z008/PeTtfvDBB+YLL7xgLlmyxFy9erX597//3fT5fOb999/f5B8TqqpPx2vWrDEffvhhc+HCheb69evNWbNmmb179zZPOeWUytfha+rIUZ+Oi4qKzLS0NHPIkCHmmjVrqvx7XF5ebppmZB/HDHobueSSS8wTTjjhsC9bv369Kcn86quvKm/bv3+/+fvf/95s166d6fP5zPPOO8/ctm1bldfbsGGDOW7cOLNVq1am3+83b7311ipPz4DmU1ZWZt56661mUlKS2aZNG/OMM84wly5dWuU+ksypU6dWue2f//yn2apVK7OwsPCQt7lo0SIzLS3NTEhIMGNjY82BAweajz76KI+9tUhdOw6FQuaZZ55pdujQwfR6vWaPHj3M66+/vsrT4pimae7atcu85JJLzNatW5vx8fHm1Vdfbe7du7e5Piz8rK79Hvz/9uF+Hfx/OcdwZKnP/6crKirM++67z+zYsaMZExNjnn766eaqVauqvA7HcGSozfF2uK+3TNM077rrLjM5OdkMh8OHvN1PP/3UTE1NNVu3bm3GxcWZKSkp5vPPP3/Y+6Jp1afjTZs2maeccorZvn17MyYmxuzbt695++23H3K9E76mjgz16firr76q9t/j9evXm6YZ2cexyzQj4Fr7AAAAAACgTngeegAAAAAAbIhBDwAAAACADTHoAQAAAACwIQY9AAAAAAA2xKAHAAAAAMCGGPQAAAAAANgQgx4AAAAAABti0AMAAAAAYEMMegAAAAAAbIhBDwAALLNr1y4lJSVpw4YN1d5n1KhRmjRpUp3f9q9//Ws9/fTT9Q8HAECEY9ADAOBQp556qlwu1yG/rrjiilq9/tVXX6177733kLf31ltvVbnfX//6V3Xp0qVeGR955BGdc8456tmzZ61f56qrrqry8SQmJmrs2LFavHhxlfvde++9euSRR1RUVFSvbAAARDoGPQAADmSapn766Sc99dRT2rZtW5Vff//732t8/XA4rI8++kgTJkyo8vY6d+6sd999t8p9Fy1apGOOOabOGUOhkF566SVde+21dX7dsWPHVn48s2fPVlRUlM4+++wq9xk8eLD69OmjN954o85vHwAAO2DQAwDgQKtXr9bevXt1yimnqFOnTlV+tW7dusbXnzNnjrxer4477rgqb+/ee+/Vp59+qlAoVHnfH3/8UcOHD69zxk8++UQxMTE6/vjjK28rLi7WFVdcodatW6tz587VnjIfExNT+fGkpqZqypQp2rx5s3bu3FnlfuPHj9fbb79d52wAANgBgx4AAAdatGiRoqKiNHTo0Hq9/gcffKDx48fL5XJVvr3Y2Fhdd911io+P16effipJKikp0YoVK+r1E/pvv/32kG8E3H777frPf/6jWbNm6fPPP9fXX3+tH3/88YhvZ9++fXrjjf/f3v2ERLnFYRx/lJmxaIYgHSKnwUWKaVqaOC1yoYiFloa2che4aJMQhe7btEnbBLnIFsLd9I9yUtQWFQiCQ/OCw/iHoqDFiEQ0FpbNMKN3cfGlFxslb9x8vd8PvIs5c86Zc2blw5nz8y8VFhYqNzfX8l4gEFAoFFIikfjl9QEAsN0R6AEA2IEMw1A6nVZubq7cbrf5XLx4UZI0PDysS5cuZRw/ODho/tx+bb6jR4/K5XKptbVVDx8+lCRNTU0plUqZgX5oaEjFxcUqKipSf3//hmt8//695e790tKS7t69q56eHtXX16u8vFwDAwNKpVLrxg4NDZl78ng8CgaDunfvnrKzrX/a5OfnK5lMamFhYZNvDAAA+3H86QUAAIDfzzAMtbe369q1a5b2ffv2SZIikYgqKip+OnZ2dlbz8/Oqr6+3zLcW2tva2tTW1qZEIiHDMOT1euX3+5VKpXTlyhW9ePFCe/fuVVVVlVpbW9edmq9ZXl7Wrl27zNdv375VMpnUiRMnLOstLi5eN7aurk59fX2SpHg8rtu3b6uxsVGhUEgFBQVmv927d0uS5YoAAAA7BSf0AADsQIZh6OTJkyosLLQ8Pwb6ubk5VVVVqbS0VHNzc+bYYDCohoYGS9j+8Z58bW2tnE6nxsbGLAXxQqGQjhw5Ip/PJ7fbrcbGRj179izjGvPy8hSPx7e0vz179ph7qq6uVn9/v75+/ao7d+5Y+n369EmS5PV6t/Q5AABsZwR6AAB2mHfv3mlxcVHHjh3L2CcSicjv9yscDuvy5cvq6ekx3xscHNS5c+fWzbcW3B0Oh1paWvTo0SNL0J+fn5fP5zPH+Xw+xWKxjGuorKzUzMyM+frQoUNyOp2anJw02+LxuF6/fr3pnrOyspSdna3l5WVLezQa1cGDB5WXl7fpHAAA2A2BHgCAHSYcDkuS9u/fr4WFBcuzsrKiRCKhb9++qbOzU5JUUVGhjx8/SpI+fPigV69eWf4FXDgclsvlUllZmdl2/vx5BYNBTU9Pb6kgniSdPn1a09PT5im92+1WR0eHurq69Pz5c0WjUV24cGHdvXhJSiQS5p5mZ2fV2dmppaUlNTc3W/qNj4/r1KlTW1ofAADbHXfoAQDYYdaqwhcVFVnac3Jy9OXLF83MzKikpMQMymsF7yTp6dOnCgQClhNtwzBUVlYml8tltjU0NCidTiuZTJqBPj8/33IiH4vFFAgEMq6zvLxcx48f1/37981ifTdu3DCDucfj0dWrV/X58+d1Y0dHR3XgwAFJksfj0eHDh/XgwQPV1taafb5//64nT55odHR08y8NAAAbylpdXV3904sAAAD/nYGBAV2/fl3RaFTxeFxNTU0aGRmR1+tVS0uLampq1N3d/cvzplIplZSU6OXLl2ZRvImJiYxF8aR/qu13dXUpGo3+9CT+3+jr69Pjx483vMcPAICdcUIPAMD/TCQS0dmzZ1VdXa10Oq2bN2+aReNqamrU3t6+pXkdDod6e3tVV1enlZUVdXd3bxjmJenMmTN68+aNYrGY/H7/lj43E6fTqVu3bv3WOQEA2E44oQcAAAAAwIYoigcAAAAAgA0R6AEAAAAAsCECPQAAAAAANkSgBwAAAADAhgj0AAAAAADYEIEeAAAAAAAbItADAAAAAGBDBHoAAAAAAGyIQA8AAAAAgA0R6AEAAAAAsCECPQAAAAAANvQ3BRJ9RjXjFGIAAAAASUVORK5CYII=\n" + }, + "metadata": {} + } + ], + "source": [ + "plt.figure(figsize=(12,8))\n", + "plt.xlabel(r\"$E_b/N_0$ (dB)\")\n", + "plt.ylabel(\"BLER\")\n", + "plt.grid(which=\"both\")\n", + "plt.semilogy(sim_params[\"ebno_db\"], bler)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MY82CFF9tPmP" + }, + "source": [ + "## DeepMIMO License and Citation\n", + "\n", + "A. Alkhateeb, “[DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://arxiv.org/pdf/1902.06435.pdf),” in Proc. of Information Theory and Applications Workshop (ITA), San Diego, CA, Feb. 2019.\n", + "\n", + "To use the DeepMIMO dataset, please check the license information [here](https://deepmimo.net/license/)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + }, + "colab": { + "provenance": [] + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From e3c804da2c4a5b07ffb675e10b743bb0fd8d6ad4 Mon Sep 17 00:00:00 2001 From: "J.A.Morais" <19170303+jmoraispk@users.noreply.github.com> Date: Wed, 2 Jul 2025 17:05:37 -0400 Subject: [PATCH 6/7] Delete DeepMIMOv4_tutorial_sionna.ipynb --- DeepMIMOv4_tutorial_sionna.ipynb | 635 ------------------------------- 1 file changed, 635 deletions(-) delete mode 100644 DeepMIMOv4_tutorial_sionna.ipynb diff --git a/DeepMIMOv4_tutorial_sionna.ipynb b/DeepMIMOv4_tutorial_sionna.ipynb deleted file mode 100644 index c35001783..000000000 --- a/DeepMIMOv4_tutorial_sionna.ipynb +++ /dev/null @@ -1,635 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "lSbcn0aLtPmH" - }, - "source": [ - "# Using the DeepMIMO Dataset with Sionna\n", - "\n", - "In this example, you will learn how to use the ray-tracing based DeepMIMO dataset." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Thv9PInhtPmI" - }, - "source": [ - "[DeepMIMO](https://deepmimo.net/) is a generic dataset that enables a wide range of machine/deep learning applications for MIMO systems. It takes as input a set of parameters (such as antenna array configurations and time-domain/OFDM parameters) and generates MIMO channel realizations, corresponding locations, angles of arrival/departure, etc., based on these parameters and on a ray-tracing scenario selected [from those available in DeepMIMO](https://deepmimo.net/scenarios/).\n", - "\n", - "## Table of Contents\n", - "* [GPU Configuration and Imports](#GPU-Configuration-and-Imports)\n", - "* [Configuration of DeepMIMO](#Configuration-of-DeepMIMO)\n", - "* [Using DeepMIMO with Sionna](#Using-DeepMIMO-with-Sionna)\n", - "* [Link-level Simulations using Sionna and DeepMIMO](#Link-level-Simulations-using-Sionna-and-DeepMIMO)\n", - "* [DeepMIMO License and Citation](#DeepMIMO-License-and-Citation)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "G-qy_3RatPmI" - }, - "source": [ - "## GPU Configuration and Imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:42.830858Z", - "iopub.status.busy": "2025-03-13T01:48:42.830249Z", - "iopub.status.idle": "2025-03-13T01:48:45.899584Z", - "shell.execute_reply": "2025-03-13T01:48:45.898771Z" - }, - "id": "yuejsob6tPmJ" - }, - "outputs": [], - "source": [ - "import os\n", - "if os.getenv(\"CUDA_VISIBLE_DEVICES\") is None:\n", - " gpu_num = 0 # Use \"\" to use the CPU\n", - " os.environ[\"CUDA_VISIBLE_DEVICES\"] = f\"{gpu_num}\"\n", - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n", - "\n", - "# Import Sionna\n", - "try:\n", - " import sionna.phy\n", - "except ImportError as e:\n", - " import sys\n", - " if 'google.colab' in sys.modules:\n", - " # Install Sionna in Google Colab\n", - " print(\"Installing Sionna and restarting the runtime. Please run the cell again.\")\n", - " os.system(\"pip install sionna\")\n", - " os.kill(os.getpid(), 5)\n", - " else:\n", - " raise e\n", - "\n", - "# Configure the notebook to use only a single GPU and allocate only as much memory as needed\n", - "# For more details, see https://www.tensorflow.org/guide/gpu\n", - "import tensorflow as tf\n", - "gpus = tf.config.list_physical_devices('GPU')\n", - "if gpus:\n", - " try:\n", - " tf.config.experimental.set_memory_growth(gpus[0], True)\n", - " except RuntimeError as e:\n", - " print(e)\n", - "# Avoid warnings from TensorFlow\n", - "tf.get_logger().setLevel('ERROR')\n", - "\n", - "sionna.phy.config.seed = 42 # Set seed for reproducible random number generation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:45.903639Z", - "iopub.status.busy": "2025-03-13T01:48:45.903247Z", - "iopub.status.idle": "2025-03-13T01:48:45.914833Z", - "shell.execute_reply": "2025-03-13T01:48:45.913951Z" - }, - "id": "kZ21nyrJtPmJ" - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Load the required Sionna components\n", - "from sionna.phy import Block\n", - "from sionna.phy.mimo import StreamManagement\n", - "from sionna.phy.ofdm import ResourceGrid, ResourceGridMapper, LSChannelEstimator, \\\n", - " LMMSEEqualizer, RZFPrecoder, RemoveNulledSubcarriers\n", - "from sionna.phy.channel import subcarrier_frequencies, ApplyOFDMChannel, \\\n", - " GenerateOFDMChannel, CIRDataset\n", - "from sionna.phy.fec.ldpc import LDPC5GEncoder, LDPC5GDecoder\n", - "from sionna.phy.mapping import BinarySource, Mapper, Demapper\n", - "from sionna.phy.utils import ebnodb2no, sim_ber\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6Ze8zPzztPmJ" - }, - "source": [ - "## Configuration of DeepMIMO" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "12rSAfnUtPmJ" - }, - "source": [ - "DeepMIMO provides multiple [scenarios](https://deepmimo.net/scenarios/) that one can select from. Here, we use the `O1_60` scenario, which is an instance of O1 with the carrier frequency set to 60 GHz.\n", - "\n", - "To use DeepMIMO with Sionna, the DeepMIMO dataset first needs to be generated. The generated DeepMIMO dataset contains channels for different locations of the users and basestations. The layout of the O1 scenario is shown in the figure below.\n", - "\n", - "![img](https://deepmimo.net/examples/sionna_adapter_example.png)\n", - "\n", - "In this example, we generate a dataset that consists of channels for the links from the basestation 6 to the users located on the rows 400 to 450. Each of these rows consists of 181 user locations, resulting in $51 \\times 181 = 9231$ basestation-user channels.\n", - "\n", - "The antenna arrays in the DeepMIMO dataset are defined as number of horizontal & vertical elements. In the following example, a single-user MISO downlink is considered. The basestation is equipped with a uniform linear array of 16 elements, which is oriented towards the positive x-axis by default. The users are each equipped with a single antenna. These parameters can be configured using the code below (for more information about the DeepMIMO parameters, please check [the DeepMIMO documentation](https://deepmimo.net/documentation)." - ] - }, - { - "cell_type": "code", - "source": [ - "# Import DeepMIMO (install package if import fails)\n", - "try:\n", - " import deepmimo as dm\n", - "except ImportError as e:\n", - " import os\n", - " os.system(\"pip install --pre deepmimo\")\n", - " import deepmimo as dm" - ], - "metadata": { - "id": "Y39zbDbyuQ2J" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:45.918949Z", - "iopub.status.busy": "2025-03-13T01:48:45.918713Z", - "iopub.status.idle": "2025-03-13T01:48:52.133244Z", - "shell.execute_reply": "2025-03-13T01:48:52.132383Z" - }, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "h8cNH5cGtPmN", - "outputId": "edf9507e-66a9-47f8-cd14-e3f977c3f0d0" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Downloading scenario 'o1_60'\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Downloading: 100%|██████████| 2.30G/2.30G [00:45<00:00, 54.6MB/s]\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "✓ Downloaded to /content/deepmimo_scenarios/o1_60_downloaded.zip\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Extracting: 100%|██████████| 4161/4161 [02:37<00:00, 26.34file/s] \n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "✓ Unzipped and moved to /content/deepmimo_scenarios\n", - "✓ Scenario 'o1_60' ready to use!\n", - "Loading TXRX PAIR: TXset 6 (tx_idx 0) & RXset 0 (rx_idxs 497931)\n" - ] - } - ], - "source": [ - "# Download the dataset\n", - "dm.download('O1_60')\n", - "\n", - "# Load a BS-RX grid combination\n", - "load_params = {'max_paths': 10, 'tx_sets': [6], 'rx_sets': [0]}\n", - "dataset = dm.load('O1_60', **load_params)\n", - "\n", - "# Select a subset of users in the dataset (and trim matrices)\n", - "sel_usr_idxs = dataset.get_row_idxs(range(400, 451))\n", - "dataset_t = dataset.subset(sel_usr_idxs)" - ] - }, - { - "cell_type": "code", - "source": [ - "# Channel generation\n", - "params = dm.ChannelParameters() # Load the default parameters\n", - "\n", - "# Configuration of the antenna arrays\n", - "params.bs_antenna.shape = [16, 1] # BS antenna shape [horizontal, vertical]\n", - "params.ue_antenna.shape = [1, 1] # UE antenna shape [horizontal, vertical]\n", - "\n", - "# Configure time domain channels. Sionna will generate in frequency domain\n", - "params.freq_domain = False\n", - "\n", - "# Generates a DeepMIMO dataset\n", - "dataset_t.compute_channels(params)\n", - "dataset_t.channels.shape # (num_rx, num_rx_ant, num_tx_ant, num_time)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "XkPe8gvwuPfk", - "outputId": "fe0fe508-7a82-4639-d46c-86c5b0be6967" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Generating channels: 100%|██████████| 9231/9231 [00:00<00:00, 29525.95it/s]\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(9231, 1, 16, 10)" - ] - }, - "metadata": {}, - "execution_count": 5 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dhOdsh0rtPmN" - }, - "source": [ - "### Visualization of the dataset\n", - "\n", - "To provide a better understanding of the user and basestation locations, we next visualize the locations of the users, highlighting the first active row of users (row 400), and basestation 6." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:52.136681Z", - "iopub.status.busy": "2025-03-13T01:48:52.136424Z", - "iopub.status.idle": "2025-03-13T01:48:52.372533Z", - "shell.execute_reply": "2025-03-13T01:48:52.371889Z" - }, - "colab": { - "base_uri": "https://localhost:8080/", - "height": 388 - }, - "id": "0ShXyVmItPmN", - "outputId": "0b9b8a8c-4076-408e-9027-cf0160c3a797" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], - "source": [ - " # Plot the azimuth of the AoA\n", - "dataset_t.aoa_az.plot(cbar_title='Angle of Arrival (deg)')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YlwUGc-NtPmO" - }, - "source": [ - "## Using DeepMIMO with Sionna\n", - "\n", - "The DeepMIMO Python package provides [a Sionna-compliant channel impulse response generator](https://nvlabs.github.io/sionna/phy/tutorials/CIR_Dataset.html#Generators) that adapts the structure of the DeepMIMO dataset to be consistent with Sionna.\n", - "\n", - "An adapter is instantiated for a given DeepMIMO dataset. The adapter only requires the dataset to adapt and it will generate samples for all users and basestations in that dataset.\n", - "\n", - "In this example, `SionnaAdapter(dataset)` will return the channels from the basestation 6 and the 9231 available user locations.\n", - "\n", - "**Note:** The adapter assumes basestations are transmitters and users are receivers. Uplink channels can be obtained using (transpose) reciprocity." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:52.376377Z", - "iopub.status.busy": "2025-03-13T01:48:52.376179Z", - "iopub.status.idle": "2025-03-13T01:48:52.380757Z", - "shell.execute_reply": "2025-03-13T01:48:52.380192Z" - }, - "id": "6LDSxX9PtPmO" - }, - "outputs": [], - "source": [ - "from deepmimo.integrations import SionnaAdapter\n", - "\n", - "deepmimo_sionna_adapter = SionnaAdapter(dataset_t)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rl8wZ1O-tPmO" - }, - "source": [ - "## Link-level Simulations using Sionna and DeepMIMO\n", - "\n", - "In the following cell, we define a Sionna model implementing the end-to-end link.\n", - "\n", - "**Note:** The Sionna CIRDataset object shuffles the DeepMIMO channels provided by the adapter. Therefore, channel samples are passed through the model in a random order." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:52.384454Z", - "iopub.status.busy": "2025-03-13T01:48:52.384238Z", - "iopub.status.idle": "2025-03-13T01:48:52.397002Z", - "shell.execute_reply": "2025-03-13T01:48:52.396376Z" - }, - "id": "EeoY1rP9tPmO" - }, - "outputs": [], - "source": [ - "class LinkModel(Block):\n", - " def __init__(self,\n", - " DeepMIMO_Sionna_adapter,\n", - " carrier_frequency,\n", - " cyclic_prefix_length,\n", - " pilot_ofdm_symbol_indices,\n", - " subcarrier_spacing = 60e3,\n", - " batch_size = 64\n", - " ):\n", - " super().__init__()\n", - "\n", - " self._batch_size = batch_size\n", - " self._cyclic_prefix_length = cyclic_prefix_length\n", - " self._pilot_ofdm_symbol_indices = pilot_ofdm_symbol_indices\n", - "\n", - " # CIRDataset to parse the dataset\n", - " self._CIR = CIRDataset(DeepMIMO_Sionna_adapter,\n", - " self._batch_size,\n", - " DeepMIMO_Sionna_adapter.num_rx,\n", - " DeepMIMO_Sionna_adapter.num_rx_ant,\n", - " DeepMIMO_Sionna_adapter.num_tx,\n", - " DeepMIMO_Sionna_adapter.num_tx_ant,\n", - " DeepMIMO_Sionna_adapter.num_paths,\n", - " DeepMIMO_Sionna_adapter.num_time_steps)\n", - "\n", - " # System parameters\n", - " self._carrier_frequency = carrier_frequency\n", - " self._subcarrier_spacing = subcarrier_spacing\n", - " self._fft_size = 76\n", - " self._num_ofdm_symbols = 14\n", - " self._num_streams_per_tx = DeepMIMO_Sionna_adapter.num_rx\n", - " self._dc_null = False\n", - " self._num_guard_carriers = [0, 0]\n", - " self._pilot_pattern = \"kronecker\"\n", - " self._pilot_ofdm_symbol_indices = pilot_ofdm_symbol_indices\n", - " self._num_bits_per_symbol = 4\n", - " self._coderate = 0.5\n", - "\n", - " # Setup the OFDM resource grid and stream management\n", - " self._sm = StreamManagement(np.ones([DeepMIMO_Sionna_adapter.num_rx, 1], int), self._num_streams_per_tx)\n", - " self._rg = ResourceGrid(num_ofdm_symbols=self._num_ofdm_symbols,\n", - " fft_size=self._fft_size,\n", - " subcarrier_spacing = self._subcarrier_spacing,\n", - " num_tx=DeepMIMO_Sionna_adapter.num_tx,\n", - " num_streams_per_tx=self._num_streams_per_tx,\n", - " cyclic_prefix_length=self._cyclic_prefix_length,\n", - " num_guard_carriers=self._num_guard_carriers,\n", - " dc_null=self._dc_null,\n", - " pilot_pattern=self._pilot_pattern,\n", - " pilot_ofdm_symbol_indices=self._pilot_ofdm_symbol_indices)\n", - "\n", - " # Components forming the link\n", - "\n", - " # Codeword length\n", - " self._n = int(self._rg.num_data_symbols * self._num_bits_per_symbol)\n", - " # Number of information bits per codeword\n", - " self._k = int(self._n * self._coderate)\n", - "\n", - " # OFDM channel\n", - " self._frequencies = subcarrier_frequencies(self._rg.fft_size, self._rg.subcarrier_spacing)\n", - " self._ofdm_channel = GenerateOFDMChannel(self._CIR, self._rg, normalize_channel=True)\n", - " self._channel_freq = ApplyOFDMChannel(add_awgn=True)\n", - "\n", - " # Transmitter\n", - " self._binary_source = BinarySource()\n", - " self._encoder = LDPC5GEncoder(self._k, self._n)\n", - " self._mapper = Mapper(\"qam\", self._num_bits_per_symbol)\n", - " self._rg_mapper = ResourceGridMapper(self._rg)\n", - " self._zf_precoder = RZFPrecoder(self._rg, self._sm, return_effective_channel=True)\n", - "\n", - " # Receiver\n", - " self._ls_est = LSChannelEstimator(self._rg, interpolation_type=\"lin_time_avg\")\n", - " self._lmmse_equ = LMMSEEqualizer(self._rg, self._sm)\n", - " self._demapper = Demapper(\"app\", \"qam\", self._num_bits_per_symbol)\n", - " self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n", - " self._remove_nulled_scs = RemoveNulledSubcarriers(self._rg)\n", - "\n", - " def call(self, batch_size, ebno_db):\n", - "\n", - " # Transmitter\n", - " b = self._binary_source([self._batch_size, 1, self._num_streams_per_tx, self._k])\n", - " c = self._encoder(b)\n", - " x = self._mapper(c)\n", - " x_rg = self._rg_mapper(x)\n", - " # Generate the OFDM channel\n", - " h_freq = self._ofdm_channel()\n", - " # Precoding\n", - " x_rg, g = self._zf_precoder(x_rg, h_freq)\n", - "\n", - " # Apply OFDM channel\n", - " no = ebnodb2no(ebno_db, self._num_bits_per_symbol, self._coderate, self._rg)\n", - " y = self._channel_freq(x_rg, h_freq, no)\n", - "\n", - " # Receiver\n", - " h_hat, err_var = self._ls_est (y, no)\n", - " x_hat, no_eff = self._lmmse_equ(y, h_hat, err_var, no)\n", - " llr = self._demapper(x_hat, no_eff)\n", - " b_hat = self._decoder(llr)\n", - "\n", - " return b, b_hat" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "15OdNJoLtPmO" - }, - "source": [ - "We next evaluate the setup with different $E_b/N_0$ values to obtain BLER curves." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:52.400127Z", - "iopub.status.busy": "2025-03-13T01:48:52.399905Z", - "iopub.status.idle": "2025-03-13T01:49:23.855650Z", - "shell.execute_reply": "2025-03-13T01:49:23.854765Z" - }, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "WC1tSAq9tPmP", - "outputId": "26a07782-c588-4a32-c706-364f58b3dfe6" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", - "---------------------------------------------------------------------------------------------------------------------------------------\n", - " -7.0 | 1.2781e-01 | 1.0000e+00 | 29840 | 233472 | 128 | 128 | 7.4 |reached target block errors\n", - " -6.806 | 9.7352e-02 | 1.0000e+00 | 22729 | 233472 | 128 | 128 | 1.0 |reached target block errors\n", - " -6.611 | 6.6376e-02 | 9.4531e-01 | 15497 | 233472 | 121 | 128 | 1.0 |reached target block errors\n", - " -6.417 | 3.0492e-02 | 7.9688e-01 | 7119 | 233472 | 102 | 128 | 1.0 |reached target block errors\n", - " -6.222 | 1.1498e-02 | 4.7656e-01 | 5369 | 466944 | 122 | 256 | 2.1 |reached target block errors\n", - " -6.028 | 1.4813e-03 | 1.3281e-01 | 2075 | 1400832 | 102 | 768 | 6.8 |reached target block errors\n", - " -5.833 | 1.6177e-04 | 2.2645e-02 | 1303 | 8054784 | 100 | 4416 | 38.1 |reached target block errors\n", - " -5.639 | 1.4563e-05 | 3.9062e-03 | 170 | 11673600 | 25 | 6400 | 56.0 |reached max iterations\n", - " -5.444 | 3.5979e-06 | 6.2500e-04 | 42 | 11673600 | 4 | 6400 | 54.6 |reached max iterations\n", - " -5.25 | 0.0000e+00 | 0.0000e+00 | 0 | 11673600 | 0 | 6400 | 57.5 |reached max iterations\n", - "\n", - "Simulation stopped as no error occurred @ EbNo = -5.2 dB.\n", - "\n" - ] - } - ], - "source": [ - "sim_params = {\"ebno_db\": np.linspace(-7, -5.25, 10),\n", - " \"cyclic_prefix_length\" : 0,\n", - " \"pilot_ofdm_symbol_indices\" : [2, 11]}\n", - "\n", - "batch_size = 64\n", - "model = LinkModel(DeepMIMO_Sionna_adapter=deepmimo_sionna_adapter,\n", - " carrier_frequency=dataset_t.rt_params.frequency,\n", - " cyclic_prefix_length=sim_params[\"cyclic_prefix_length\"],\n", - " pilot_ofdm_symbol_indices=sim_params[\"pilot_ofdm_symbol_indices\"])\n", - "\n", - "ber, bler = sim_ber(model,\n", - " sim_params[\"ebno_db\"],\n", - " batch_size=batch_size,\n", - " max_mc_iter=100,\n", - " num_target_block_errors=100,\n", - " graph_mode=\"graph\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:49:23.859850Z", - "iopub.status.busy": "2025-03-13T01:49:23.859631Z", - "iopub.status.idle": "2025-03-13T01:49:24.255402Z", - "shell.execute_reply": "2025-03-13T01:49:24.254518Z" - }, - "colab": { - "base_uri": "https://localhost:8080/", - "height": 716 - }, - "id": "q5G8_EU6tPmP", - "outputId": "8c3bffcc-d47d-4cde-9c65-0f6c984ea3cb" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[]" - ] - }, - "metadata": {}, - "execution_count": 11 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], - "source": [ - "plt.figure(figsize=(12,8))\n", - "plt.xlabel(r\"$E_b/N_0$ (dB)\")\n", - "plt.ylabel(\"BLER\")\n", - "plt.grid(which=\"both\")\n", - "plt.semilogy(sim_params[\"ebno_db\"], bler)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MY82CFF9tPmP" - }, - "source": [ - "## DeepMIMO License and Citation\n", - "\n", - "A. Alkhateeb, “[DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://arxiv.org/pdf/1902.06435.pdf),” in Proc. of Information Theory and Applications Workshop (ITA), San Diego, CA, Feb. 2019.\n", - "\n", - "To use the DeepMIMO dataset, please check the license information [here](https://deepmimo.net/license/)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.16" - }, - "colab": { - "provenance": [] - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file From 2dc85484438b50bc994af1aa30fa2a6e13c16f7e Mon Sep 17 00:00:00 2001 From: "J.A.Morais" <19170303+jmoraispk@users.noreply.github.com> Date: Wed, 2 Jul 2025 17:05:47 -0400 Subject: [PATCH 7/7] move to the right folder --- DeepMIMOv4.ipynb | 635 ----------------------------------- tutorials/phy/DeepMIMO.ipynb | 318 +++++++++--------- 2 files changed, 162 insertions(+), 791 deletions(-) delete mode 100644 DeepMIMOv4.ipynb diff --git a/DeepMIMOv4.ipynb b/DeepMIMOv4.ipynb deleted file mode 100644 index c35001783..000000000 --- a/DeepMIMOv4.ipynb +++ /dev/null @@ -1,635 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "lSbcn0aLtPmH" - }, - "source": [ - "# Using the DeepMIMO Dataset with Sionna\n", - "\n", - "In this example, you will learn how to use the ray-tracing based DeepMIMO dataset." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Thv9PInhtPmI" - }, - "source": [ - "[DeepMIMO](https://deepmimo.net/) is a generic dataset that enables a wide range of machine/deep learning applications for MIMO systems. It takes as input a set of parameters (such as antenna array configurations and time-domain/OFDM parameters) and generates MIMO channel realizations, corresponding locations, angles of arrival/departure, etc., based on these parameters and on a ray-tracing scenario selected [from those available in DeepMIMO](https://deepmimo.net/scenarios/).\n", - "\n", - "## Table of Contents\n", - "* [GPU Configuration and Imports](#GPU-Configuration-and-Imports)\n", - "* [Configuration of DeepMIMO](#Configuration-of-DeepMIMO)\n", - "* [Using DeepMIMO with Sionna](#Using-DeepMIMO-with-Sionna)\n", - "* [Link-level Simulations using Sionna and DeepMIMO](#Link-level-Simulations-using-Sionna-and-DeepMIMO)\n", - "* [DeepMIMO License and Citation](#DeepMIMO-License-and-Citation)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "G-qy_3RatPmI" - }, - "source": [ - "## GPU Configuration and Imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:42.830858Z", - "iopub.status.busy": "2025-03-13T01:48:42.830249Z", - "iopub.status.idle": "2025-03-13T01:48:45.899584Z", - "shell.execute_reply": "2025-03-13T01:48:45.898771Z" - }, - "id": "yuejsob6tPmJ" - }, - "outputs": [], - "source": [ - "import os\n", - "if os.getenv(\"CUDA_VISIBLE_DEVICES\") is None:\n", - " gpu_num = 0 # Use \"\" to use the CPU\n", - " os.environ[\"CUDA_VISIBLE_DEVICES\"] = f\"{gpu_num}\"\n", - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n", - "\n", - "# Import Sionna\n", - "try:\n", - " import sionna.phy\n", - "except ImportError as e:\n", - " import sys\n", - " if 'google.colab' in sys.modules:\n", - " # Install Sionna in Google Colab\n", - " print(\"Installing Sionna and restarting the runtime. Please run the cell again.\")\n", - " os.system(\"pip install sionna\")\n", - " os.kill(os.getpid(), 5)\n", - " else:\n", - " raise e\n", - "\n", - "# Configure the notebook to use only a single GPU and allocate only as much memory as needed\n", - "# For more details, see https://www.tensorflow.org/guide/gpu\n", - "import tensorflow as tf\n", - "gpus = tf.config.list_physical_devices('GPU')\n", - "if gpus:\n", - " try:\n", - " tf.config.experimental.set_memory_growth(gpus[0], True)\n", - " except RuntimeError as e:\n", - " print(e)\n", - "# Avoid warnings from TensorFlow\n", - "tf.get_logger().setLevel('ERROR')\n", - "\n", - "sionna.phy.config.seed = 42 # Set seed for reproducible random number generation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:45.903639Z", - "iopub.status.busy": "2025-03-13T01:48:45.903247Z", - "iopub.status.idle": "2025-03-13T01:48:45.914833Z", - "shell.execute_reply": "2025-03-13T01:48:45.913951Z" - }, - "id": "kZ21nyrJtPmJ" - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Load the required Sionna components\n", - "from sionna.phy import Block\n", - "from sionna.phy.mimo import StreamManagement\n", - "from sionna.phy.ofdm import ResourceGrid, ResourceGridMapper, LSChannelEstimator, \\\n", - " LMMSEEqualizer, RZFPrecoder, RemoveNulledSubcarriers\n", - "from sionna.phy.channel import subcarrier_frequencies, ApplyOFDMChannel, \\\n", - " GenerateOFDMChannel, CIRDataset\n", - "from sionna.phy.fec.ldpc import LDPC5GEncoder, LDPC5GDecoder\n", - "from sionna.phy.mapping import BinarySource, Mapper, Demapper\n", - "from sionna.phy.utils import ebnodb2no, sim_ber\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6Ze8zPzztPmJ" - }, - "source": [ - "## Configuration of DeepMIMO" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "12rSAfnUtPmJ" - }, - "source": [ - "DeepMIMO provides multiple [scenarios](https://deepmimo.net/scenarios/) that one can select from. Here, we use the `O1_60` scenario, which is an instance of O1 with the carrier frequency set to 60 GHz.\n", - "\n", - "To use DeepMIMO with Sionna, the DeepMIMO dataset first needs to be generated. The generated DeepMIMO dataset contains channels for different locations of the users and basestations. The layout of the O1 scenario is shown in the figure below.\n", - "\n", - "![img](https://deepmimo.net/examples/sionna_adapter_example.png)\n", - "\n", - "In this example, we generate a dataset that consists of channels for the links from the basestation 6 to the users located on the rows 400 to 450. Each of these rows consists of 181 user locations, resulting in $51 \\times 181 = 9231$ basestation-user channels.\n", - "\n", - "The antenna arrays in the DeepMIMO dataset are defined as number of horizontal & vertical elements. In the following example, a single-user MISO downlink is considered. The basestation is equipped with a uniform linear array of 16 elements, which is oriented towards the positive x-axis by default. The users are each equipped with a single antenna. These parameters can be configured using the code below (for more information about the DeepMIMO parameters, please check [the DeepMIMO documentation](https://deepmimo.net/documentation)." - ] - }, - { - "cell_type": "code", - "source": [ - "# Import DeepMIMO (install package if import fails)\n", - "try:\n", - " import deepmimo as dm\n", - "except ImportError as e:\n", - " import os\n", - " os.system(\"pip install --pre deepmimo\")\n", - " import deepmimo as dm" - ], - "metadata": { - "id": "Y39zbDbyuQ2J" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:45.918949Z", - "iopub.status.busy": "2025-03-13T01:48:45.918713Z", - "iopub.status.idle": "2025-03-13T01:48:52.133244Z", - "shell.execute_reply": "2025-03-13T01:48:52.132383Z" - }, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "h8cNH5cGtPmN", - "outputId": "edf9507e-66a9-47f8-cd14-e3f977c3f0d0" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Downloading scenario 'o1_60'\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Downloading: 100%|██████████| 2.30G/2.30G [00:45<00:00, 54.6MB/s]\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "✓ Downloaded to /content/deepmimo_scenarios/o1_60_downloaded.zip\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Extracting: 100%|██████████| 4161/4161 [02:37<00:00, 26.34file/s] \n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "✓ Unzipped and moved to /content/deepmimo_scenarios\n", - "✓ Scenario 'o1_60' ready to use!\n", - "Loading TXRX PAIR: TXset 6 (tx_idx 0) & RXset 0 (rx_idxs 497931)\n" - ] - } - ], - "source": [ - "# Download the dataset\n", - "dm.download('O1_60')\n", - "\n", - "# Load a BS-RX grid combination\n", - "load_params = {'max_paths': 10, 'tx_sets': [6], 'rx_sets': [0]}\n", - "dataset = dm.load('O1_60', **load_params)\n", - "\n", - "# Select a subset of users in the dataset (and trim matrices)\n", - "sel_usr_idxs = dataset.get_row_idxs(range(400, 451))\n", - "dataset_t = dataset.subset(sel_usr_idxs)" - ] - }, - { - "cell_type": "code", - "source": [ - "# Channel generation\n", - "params = dm.ChannelParameters() # Load the default parameters\n", - "\n", - "# Configuration of the antenna arrays\n", - "params.bs_antenna.shape = [16, 1] # BS antenna shape [horizontal, vertical]\n", - "params.ue_antenna.shape = [1, 1] # UE antenna shape [horizontal, vertical]\n", - "\n", - "# Configure time domain channels. Sionna will generate in frequency domain\n", - "params.freq_domain = False\n", - "\n", - "# Generates a DeepMIMO dataset\n", - "dataset_t.compute_channels(params)\n", - "dataset_t.channels.shape # (num_rx, num_rx_ant, num_tx_ant, num_time)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "XkPe8gvwuPfk", - "outputId": "fe0fe508-7a82-4639-d46c-86c5b0be6967" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Generating channels: 100%|██████████| 9231/9231 [00:00<00:00, 29525.95it/s]\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(9231, 1, 16, 10)" - ] - }, - "metadata": {}, - "execution_count": 5 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dhOdsh0rtPmN" - }, - "source": [ - "### Visualization of the dataset\n", - "\n", - "To provide a better understanding of the user and basestation locations, we next visualize the locations of the users, highlighting the first active row of users (row 400), and basestation 6." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:52.136681Z", - "iopub.status.busy": "2025-03-13T01:48:52.136424Z", - "iopub.status.idle": "2025-03-13T01:48:52.372533Z", - "shell.execute_reply": "2025-03-13T01:48:52.371889Z" - }, - "colab": { - "base_uri": "https://localhost:8080/", - "height": 388 - }, - "id": "0ShXyVmItPmN", - "outputId": "0b9b8a8c-4076-408e-9027-cf0160c3a797" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], - "source": [ - " # Plot the azimuth of the AoA\n", - "dataset_t.aoa_az.plot(cbar_title='Angle of Arrival (deg)')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YlwUGc-NtPmO" - }, - "source": [ - "## Using DeepMIMO with Sionna\n", - "\n", - "The DeepMIMO Python package provides [a Sionna-compliant channel impulse response generator](https://nvlabs.github.io/sionna/phy/tutorials/CIR_Dataset.html#Generators) that adapts the structure of the DeepMIMO dataset to be consistent with Sionna.\n", - "\n", - "An adapter is instantiated for a given DeepMIMO dataset. The adapter only requires the dataset to adapt and it will generate samples for all users and basestations in that dataset.\n", - "\n", - "In this example, `SionnaAdapter(dataset)` will return the channels from the basestation 6 and the 9231 available user locations.\n", - "\n", - "**Note:** The adapter assumes basestations are transmitters and users are receivers. Uplink channels can be obtained using (transpose) reciprocity." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:52.376377Z", - "iopub.status.busy": "2025-03-13T01:48:52.376179Z", - "iopub.status.idle": "2025-03-13T01:48:52.380757Z", - "shell.execute_reply": "2025-03-13T01:48:52.380192Z" - }, - "id": "6LDSxX9PtPmO" - }, - "outputs": [], - "source": [ - "from deepmimo.integrations import SionnaAdapter\n", - "\n", - "deepmimo_sionna_adapter = SionnaAdapter(dataset_t)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rl8wZ1O-tPmO" - }, - "source": [ - "## Link-level Simulations using Sionna and DeepMIMO\n", - "\n", - "In the following cell, we define a Sionna model implementing the end-to-end link.\n", - "\n", - "**Note:** The Sionna CIRDataset object shuffles the DeepMIMO channels provided by the adapter. Therefore, channel samples are passed through the model in a random order." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:52.384454Z", - "iopub.status.busy": "2025-03-13T01:48:52.384238Z", - "iopub.status.idle": "2025-03-13T01:48:52.397002Z", - "shell.execute_reply": "2025-03-13T01:48:52.396376Z" - }, - "id": "EeoY1rP9tPmO" - }, - "outputs": [], - "source": [ - "class LinkModel(Block):\n", - " def __init__(self,\n", - " DeepMIMO_Sionna_adapter,\n", - " carrier_frequency,\n", - " cyclic_prefix_length,\n", - " pilot_ofdm_symbol_indices,\n", - " subcarrier_spacing = 60e3,\n", - " batch_size = 64\n", - " ):\n", - " super().__init__()\n", - "\n", - " self._batch_size = batch_size\n", - " self._cyclic_prefix_length = cyclic_prefix_length\n", - " self._pilot_ofdm_symbol_indices = pilot_ofdm_symbol_indices\n", - "\n", - " # CIRDataset to parse the dataset\n", - " self._CIR = CIRDataset(DeepMIMO_Sionna_adapter,\n", - " self._batch_size,\n", - " DeepMIMO_Sionna_adapter.num_rx,\n", - " DeepMIMO_Sionna_adapter.num_rx_ant,\n", - " DeepMIMO_Sionna_adapter.num_tx,\n", - " DeepMIMO_Sionna_adapter.num_tx_ant,\n", - " DeepMIMO_Sionna_adapter.num_paths,\n", - " DeepMIMO_Sionna_adapter.num_time_steps)\n", - "\n", - " # System parameters\n", - " self._carrier_frequency = carrier_frequency\n", - " self._subcarrier_spacing = subcarrier_spacing\n", - " self._fft_size = 76\n", - " self._num_ofdm_symbols = 14\n", - " self._num_streams_per_tx = DeepMIMO_Sionna_adapter.num_rx\n", - " self._dc_null = False\n", - " self._num_guard_carriers = [0, 0]\n", - " self._pilot_pattern = \"kronecker\"\n", - " self._pilot_ofdm_symbol_indices = pilot_ofdm_symbol_indices\n", - " self._num_bits_per_symbol = 4\n", - " self._coderate = 0.5\n", - "\n", - " # Setup the OFDM resource grid and stream management\n", - " self._sm = StreamManagement(np.ones([DeepMIMO_Sionna_adapter.num_rx, 1], int), self._num_streams_per_tx)\n", - " self._rg = ResourceGrid(num_ofdm_symbols=self._num_ofdm_symbols,\n", - " fft_size=self._fft_size,\n", - " subcarrier_spacing = self._subcarrier_spacing,\n", - " num_tx=DeepMIMO_Sionna_adapter.num_tx,\n", - " num_streams_per_tx=self._num_streams_per_tx,\n", - " cyclic_prefix_length=self._cyclic_prefix_length,\n", - " num_guard_carriers=self._num_guard_carriers,\n", - " dc_null=self._dc_null,\n", - " pilot_pattern=self._pilot_pattern,\n", - " pilot_ofdm_symbol_indices=self._pilot_ofdm_symbol_indices)\n", - "\n", - " # Components forming the link\n", - "\n", - " # Codeword length\n", - " self._n = int(self._rg.num_data_symbols * self._num_bits_per_symbol)\n", - " # Number of information bits per codeword\n", - " self._k = int(self._n * self._coderate)\n", - "\n", - " # OFDM channel\n", - " self._frequencies = subcarrier_frequencies(self._rg.fft_size, self._rg.subcarrier_spacing)\n", - " self._ofdm_channel = GenerateOFDMChannel(self._CIR, self._rg, normalize_channel=True)\n", - " self._channel_freq = ApplyOFDMChannel(add_awgn=True)\n", - "\n", - " # Transmitter\n", - " self._binary_source = BinarySource()\n", - " self._encoder = LDPC5GEncoder(self._k, self._n)\n", - " self._mapper = Mapper(\"qam\", self._num_bits_per_symbol)\n", - " self._rg_mapper = ResourceGridMapper(self._rg)\n", - " self._zf_precoder = RZFPrecoder(self._rg, self._sm, return_effective_channel=True)\n", - "\n", - " # Receiver\n", - " self._ls_est = LSChannelEstimator(self._rg, interpolation_type=\"lin_time_avg\")\n", - " self._lmmse_equ = LMMSEEqualizer(self._rg, self._sm)\n", - " self._demapper = Demapper(\"app\", \"qam\", self._num_bits_per_symbol)\n", - " self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n", - " self._remove_nulled_scs = RemoveNulledSubcarriers(self._rg)\n", - "\n", - " def call(self, batch_size, ebno_db):\n", - "\n", - " # Transmitter\n", - " b = self._binary_source([self._batch_size, 1, self._num_streams_per_tx, self._k])\n", - " c = self._encoder(b)\n", - " x = self._mapper(c)\n", - " x_rg = self._rg_mapper(x)\n", - " # Generate the OFDM channel\n", - " h_freq = self._ofdm_channel()\n", - " # Precoding\n", - " x_rg, g = self._zf_precoder(x_rg, h_freq)\n", - "\n", - " # Apply OFDM channel\n", - " no = ebnodb2no(ebno_db, self._num_bits_per_symbol, self._coderate, self._rg)\n", - " y = self._channel_freq(x_rg, h_freq, no)\n", - "\n", - " # Receiver\n", - " h_hat, err_var = self._ls_est (y, no)\n", - " x_hat, no_eff = self._lmmse_equ(y, h_hat, err_var, no)\n", - " llr = self._demapper(x_hat, no_eff)\n", - " b_hat = self._decoder(llr)\n", - "\n", - " return b, b_hat" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "15OdNJoLtPmO" - }, - "source": [ - "We next evaluate the setup with different $E_b/N_0$ values to obtain BLER curves." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:48:52.400127Z", - "iopub.status.busy": "2025-03-13T01:48:52.399905Z", - "iopub.status.idle": "2025-03-13T01:49:23.855650Z", - "shell.execute_reply": "2025-03-13T01:49:23.854765Z" - }, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "WC1tSAq9tPmP", - "outputId": "26a07782-c588-4a32-c706-364f58b3dfe6" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", - "---------------------------------------------------------------------------------------------------------------------------------------\n", - " -7.0 | 1.2781e-01 | 1.0000e+00 | 29840 | 233472 | 128 | 128 | 7.4 |reached target block errors\n", - " -6.806 | 9.7352e-02 | 1.0000e+00 | 22729 | 233472 | 128 | 128 | 1.0 |reached target block errors\n", - " -6.611 | 6.6376e-02 | 9.4531e-01 | 15497 | 233472 | 121 | 128 | 1.0 |reached target block errors\n", - " -6.417 | 3.0492e-02 | 7.9688e-01 | 7119 | 233472 | 102 | 128 | 1.0 |reached target block errors\n", - " -6.222 | 1.1498e-02 | 4.7656e-01 | 5369 | 466944 | 122 | 256 | 2.1 |reached target block errors\n", - " -6.028 | 1.4813e-03 | 1.3281e-01 | 2075 | 1400832 | 102 | 768 | 6.8 |reached target block errors\n", - " -5.833 | 1.6177e-04 | 2.2645e-02 | 1303 | 8054784 | 100 | 4416 | 38.1 |reached target block errors\n", - " -5.639 | 1.4563e-05 | 3.9062e-03 | 170 | 11673600 | 25 | 6400 | 56.0 |reached max iterations\n", - " -5.444 | 3.5979e-06 | 6.2500e-04 | 42 | 11673600 | 4 | 6400 | 54.6 |reached max iterations\n", - " -5.25 | 0.0000e+00 | 0.0000e+00 | 0 | 11673600 | 0 | 6400 | 57.5 |reached max iterations\n", - "\n", - "Simulation stopped as no error occurred @ EbNo = -5.2 dB.\n", - "\n" - ] - } - ], - "source": [ - "sim_params = {\"ebno_db\": np.linspace(-7, -5.25, 10),\n", - " \"cyclic_prefix_length\" : 0,\n", - " \"pilot_ofdm_symbol_indices\" : [2, 11]}\n", - "\n", - "batch_size = 64\n", - "model = LinkModel(DeepMIMO_Sionna_adapter=deepmimo_sionna_adapter,\n", - " carrier_frequency=dataset_t.rt_params.frequency,\n", - " cyclic_prefix_length=sim_params[\"cyclic_prefix_length\"],\n", - " pilot_ofdm_symbol_indices=sim_params[\"pilot_ofdm_symbol_indices\"])\n", - "\n", - "ber, bler = sim_ber(model,\n", - " sim_params[\"ebno_db\"],\n", - " batch_size=batch_size,\n", - " max_mc_iter=100,\n", - " num_target_block_errors=100,\n", - " graph_mode=\"graph\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2025-03-13T01:49:23.859850Z", - "iopub.status.busy": "2025-03-13T01:49:23.859631Z", - "iopub.status.idle": "2025-03-13T01:49:24.255402Z", - "shell.execute_reply": "2025-03-13T01:49:24.254518Z" - }, - "colab": { - "base_uri": "https://localhost:8080/", - "height": 716 - }, - "id": "q5G8_EU6tPmP", - "outputId": "8c3bffcc-d47d-4cde-9c65-0f6c984ea3cb" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[]" - ] - }, - "metadata": {}, - "execution_count": 11 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], - "source": [ - "plt.figure(figsize=(12,8))\n", - "plt.xlabel(r\"$E_b/N_0$ (dB)\")\n", - "plt.ylabel(\"BLER\")\n", - "plt.grid(which=\"both\")\n", - "plt.semilogy(sim_params[\"ebno_db\"], bler)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MY82CFF9tPmP" - }, - "source": [ - "## DeepMIMO License and Citation\n", - "\n", - "A. Alkhateeb, “[DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://arxiv.org/pdf/1902.06435.pdf),” in Proc. of Information Theory and Applications Workshop (ITA), San Diego, CA, Feb. 2019.\n", - "\n", - "To use the DeepMIMO dataset, please check the license information [here](https://deepmimo.net/license/)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.16" - }, - "colab": { - "provenance": [] - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/tutorials/phy/DeepMIMO.ipynb b/tutorials/phy/DeepMIMO.ipynb index a3519093b..c35001783 100644 --- a/tutorials/phy/DeepMIMO.ipynb +++ b/tutorials/phy/DeepMIMO.ipynb @@ -3,7 +3,7 @@ { "cell_type": "markdown", "metadata": { - "id": "xbl2LfNzutih" + "id": "lSbcn0aLtPmH" }, "source": [ "# Using the DeepMIMO Dataset with Sionna\n", @@ -14,7 +14,7 @@ { "cell_type": "markdown", "metadata": { - "id": "2kLRRfIfutii" + "id": "Thv9PInhtPmI" }, "source": [ "[DeepMIMO](https://deepmimo.net/) is a generic dataset that enables a wide range of machine/deep learning applications for MIMO systems. It takes as input a set of parameters (such as antenna array configurations and time-domain/OFDM parameters) and generates MIMO channel realizations, corresponding locations, angles of arrival/departure, etc., based on these parameters and on a ray-tracing scenario selected [from those available in DeepMIMO](https://deepmimo.net/scenarios/).\n", @@ -30,7 +30,7 @@ { "cell_type": "markdown", "metadata": { - "id": "-yMoEzSnutii" + "id": "G-qy_3RatPmI" }, "source": [ "## GPU Configuration and Imports" @@ -46,7 +46,7 @@ "iopub.status.idle": "2025-03-13T01:48:45.899584Z", "shell.execute_reply": "2025-03-13T01:48:45.898771Z" }, - "id": "LcvoCE4Tutij" + "id": "yuejsob6tPmJ" }, "outputs": [], "source": [ @@ -94,7 +94,7 @@ "iopub.status.idle": "2025-03-13T01:48:45.914833Z", "shell.execute_reply": "2025-03-13T01:48:45.913951Z" }, - "id": "oS5pgNWqutij" + "id": "kZ21nyrJtPmJ" }, "outputs": [], "source": [ @@ -117,7 +117,7 @@ { "cell_type": "markdown", "metadata": { - "id": "NrN65-MZutij" + "id": "6Ze8zPzztPmJ" }, "source": [ "## Configuration of DeepMIMO" @@ -126,20 +126,37 @@ { "cell_type": "markdown", "metadata": { - "id": "NUDT2tKRutij" + "id": "12rSAfnUtPmJ" }, "source": [ - "DeepMIMO provides multiple [scenarios](https://deepmimo.net/scenarios/) that one can select from. In this example, we use the O1 scenario with the carrier frequency set to 60 GHz (O1_60). To run this example, please download the \"O1_60\" data files [from this page](https://deepmimo.net/scenarios/o1-scenario/). The downloaded zip file should be extracted into a folder, and the parameter `DeepMIMO_params['dataset_folder']` should be set to point to this folder, as done below.\n", + "DeepMIMO provides multiple [scenarios](https://deepmimo.net/scenarios/) that one can select from. Here, we use the `O1_60` scenario, which is an instance of O1 with the carrier frequency set to 60 GHz.\n", "\n", - "To use DeepMIMO with Sionna, the DeepMIMO dataset first needs to be generated. The generated DeepMIMO dataset contains channels for different locations of the users and basestations. The layout of the O1 scenario is shown in the figure below.\n", + "To use DeepMIMO with Sionna, the DeepMIMO dataset first needs to be generated. The generated DeepMIMO dataset contains channels for different locations of the users and basestations. The layout of the O1 scenario is shown in the figure below.\n", "\n", - 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+ "![img](https://deepmimo.net/examples/sionna_adapter_example.png)\n", "\n", "In this example, we generate a dataset that consists of channels for the links from the basestation 6 to the users located on the rows 400 to 450. Each of these rows consists of 181 user locations, resulting in $51 \\times 181 = 9231$ basestation-user channels.\n", "\n", - "The antenna arrays in the DeepMIMO dataset are defined through the x-y-z axes. In the following example, a single-user MISO downlink is considered. The basestation is equipped with a uniform linear array of 16 elements spread along the x-axis. The users are each equipped with a single antenna. These parameters can be configured using the code below (for more information about the DeepMIMO parameters, please check [the DeepMIMO configurations](https://deepmimo.net/versions/v2-python/))." + "The antenna arrays in the DeepMIMO dataset are defined as number of horizontal & vertical elements. In the following example, a single-user MISO downlink is considered. The basestation is equipped with a uniform linear array of 16 elements, which is oriented towards the positive x-axis by default. The users are each equipped with a single antenna. These parameters can be configured using the code below (for more information about the DeepMIMO parameters, please check [the DeepMIMO documentation](https://deepmimo.net/documentation)." ] }, + { + "cell_type": "code", + "source": [ + "# Import DeepMIMO (install package if import fails)\n", + "try:\n", + " import deepmimo as dm\n", + "except ImportError as e:\n", + " import os\n", + " os.system(\"pip install --pre deepmimo\")\n", + " import deepmimo as dm" + ], + "metadata": { + "id": "Y39zbDbyuQ2J" + }, + "execution_count": null, + "outputs": [] + }, { "cell_type": "code", "execution_count": null, @@ -150,84 +167,113 @@ "iopub.status.idle": "2025-03-13T01:48:52.133244Z", "shell.execute_reply": "2025-03-13T01:48:52.132383Z" }, - "id": "jtbZswoSutim", - "outputId": "b300a422-9bd1-4f4a-954a-3431211515b9" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "h8cNH5cGtPmN", + "outputId": "edf9507e-66a9-47f8-cd14-e3f977c3f0d0" }, "outputs": [ { + "output_type": "stream", "name": "stdout", + "text": [ + "Downloading scenario 'o1_60'\n" + ] + }, + { "output_type": "stream", + "name": "stderr", "text": [ - "Collecting DeepMIMO\n", - " Downloading DeepMIMO-1.0-py3-none-any.whl.metadata (421 bytes)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.10/site-packages (from DeepMIMO) (1.26.4)\n", - "Requirement already satisfied: scipy in /usr/local/lib/python3.10/site-packages (from DeepMIMO) (1.15.2)\n", - "Collecting tqdm (from DeepMIMO)\n", - " Downloading tqdm-4.67.1-py3-none-any.whl.metadata (57 kB)\n", - "Downloading DeepMIMO-1.0-py3-none-any.whl (21 kB)\n", - "Downloading tqdm-4.67.1-py3-none-any.whl (78 kB)\n", - "Installing collected packages: tqdm, DeepMIMO\n", - "Successfully installed DeepMIMO-1.0 tqdm-4.67.1\n" + "Downloading: 100%|██████████| 2.30G/2.30G [00:45<00:00, 54.6MB/s]\n" ] }, { + "output_type": "stream", "name": "stdout", + "text": [ + "✓ Downloaded to /content/deepmimo_scenarios/o1_60_downloaded.zip\n" + ] + }, + { "output_type": "stream", + "name": "stderr", "text": [ - "\n", - "Basestation 6\n", - "\n", - "UE-BS Channels\n" + "Extracting: 100%|██████████| 4161/4161 [02:37<00:00, 26.34file/s] \n" ] }, { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "\n", - "BS-BS Channels\n" + "✓ Unzipped and moved to /content/deepmimo_scenarios\n", + "✓ Scenario 'o1_60' ready to use!\n", + "Loading TXRX PAIR: TXset 6 (tx_idx 0) & RXset 0 (rx_idxs 497931)\n" ] } ], "source": [ - "# Import DeepMIMO\n", - "try:\n", - " import DeepMIMO\n", - "except ImportError as e:\n", - " # Install DeepMIMO if package is not already installed\n", - " import os\n", - " os.system(\"uv pip install --pre deepmimo\")\n", - " import DeepMIMO\n", + "# Download the dataset\n", + "dm.download('O1_60')\n", "\n", - "# Channel generation\n", - "DeepMIMO_params = DeepMIMO.default_params() # Load the default parameters\n", - "DeepMIMO_params['dataset_folder'] = r'./scenarios' # Path to the downloaded scenarios\n", - "DeepMIMO_params['scenario'] = 'O1_60' # DeepMIMO scenario\n", - "DeepMIMO_params['num_paths'] = 10 # Maximum number of paths\n", - "DeepMIMO_params['active_BS'] = np.array([6]) # Basestation indices to be included in the dataset\n", + "# Load a BS-RX grid combination\n", + "load_params = {'max_paths': 10, 'tx_sets': [6], 'rx_sets': [0]}\n", + "dataset = dm.load('O1_60', **load_params)\n", "\n", - "# Selected rows of users, whose channels are to be generated.\n", - "DeepMIMO_params['user_row_first'] = 400 # First user row to be included in the dataset\n", - "DeepMIMO_params['user_row_last'] = 450 # Last user row to be included in the dataset\n", + "# Select a subset of users in the dataset (and trim matrices)\n", + "sel_usr_idxs = dataset.get_row_idxs(range(400, 451))\n", + "dataset_t = dataset.subset(sel_usr_idxs)" + ] + }, + { + "cell_type": "code", + "source": [ + "# Channel generation\n", + "params = dm.ChannelParameters() # Load the default parameters\n", "\n", "# Configuration of the antenna arrays\n", - "DeepMIMO_params['bs_antenna']['shape'] = np.array([16, 1, 1]) # BS antenna shape through [x, y, z] axes\n", - "DeepMIMO_params['ue_antenna']['shape'] = np.array([1, 1, 1]) # UE antenna shape through [x, y, z] axes\n", + "params.bs_antenna.shape = [16, 1] # BS antenna shape [horizontal, vertical]\n", + "params.ue_antenna.shape = [1, 1] # UE antenna shape [horizontal, vertical]\n", "\n", - "# The OFDM_channels parameter allows choosing between the generation of channel impulse\n", - "# responses (if set to 0) or frequency domain channels (if set to 1).\n", - "# It is set to 0 for this simulation, as the channel responses in frequency domain\n", - "# will be generated using Sionna.\n", - "DeepMIMO_params['OFDM_channels'] = 0\n", + "# Configure time domain channels. Sionna will generate in frequency domain\n", + "params.freq_domain = False\n", "\n", "# Generates a DeepMIMO dataset\n", - "DeepMIMO_dataset = DeepMIMO.generate_data(DeepMIMO_params)" + "dataset_t.compute_channels(params)\n", + "dataset_t.channels.shape # (num_rx, num_rx_ant, num_tx_ant, num_time)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XkPe8gvwuPfk", + "outputId": "fe0fe508-7a82-4639-d46c-86c5b0be6967" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Generating channels: 100%|██████████| 9231/9231 [00:00<00:00, 29525.95it/s]\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(9231, 1, 16, 10)" + ] + }, + "metadata": {}, + "execution_count": 5 + } ] }, { "cell_type": "markdown", "metadata": { - "id": "x1G0ERliutim" + "id": "dhOdsh0rtPmN" }, "source": [ "### Visualization of the dataset\n", @@ -245,79 +291,45 @@ "iopub.status.idle": "2025-03-13T01:48:52.372533Z", "shell.execute_reply": "2025-03-13T01:48:52.371889Z" }, - "id": "qtqlhXIxutin", - "outputId": "3fdcce80-0cc8-415d-fb9c-9a17e1092cb8" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 388 + }, + "id": "0ShXyVmItPmN", + "outputId": "0b9b8a8c-4076-408e-9027-cf0160c3a797" }, "outputs": [ { + "output_type": "display_data", "data": { - "image/png": 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", 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XLmDcuHEoLi7G6NGjy1wvgwoiIiIVRKh/l4dYMU1x2b59+7Bw4UKMHTtWkf7+++9j+/bt2LhxI9q2bYs33nhDVVDh0VtK586dC0EQFFPLli0BANeuXcOECRPQokUL1KlTB40aNcLEiRORl5enqMN2eUEQsG7dOk9sDhER1QKWngq1U1Xy3XffoXfv3nbpvXr1wnfffQcAeOihh/Drr7+qqtfjPRUJCQnYsWOH9NnLy9SkrKwsZGVl4dVXX0V8fDwuXryIsWPHIisrC1988YWijtWrV6Nfv37S5+Dg4EppOxER1T414fJH/fr1sWXLFkyePFmRvmXLFtSvXx8A8Ndff6l+AJbHgwovLy9ERETYpbdu3RobN26UPsfGxmLRokUYMmQIiouLpeADMAURjuogIiJyt5oQVKSmpmLcuHHYtWuXNKbiyJEj2LZtG9577z0AQHp6Onr06KGqXo8/UfPs2bOIiopC06ZNkZycjEuXLjktm5eXB51OpwgoACAlJQUNGjRAx44dsWrVKohiyVevioqKoNfrFRMREVFtMXr0aOzevRsBAQH48ssv8eWXX8Lf3x+7d+/GqFGjAAAvvvgiPv/8c1X1erSnolOnTvjoo4/QokULZGdnY968ebj//vtx8uRJuy6XP//8EwsWLMBzzz2nSJ8/fz569uwJf39/bN++HePHj0d+fj4mTpzodL1paWmYN29ehWwTERHVbDWhpwIAunXrhm7durm1TkEs7Wd9Jbpx4wZiYmKwbNkyKVICAL1ej7///e+oX78+vvrqK6ePGAVMt8msXr0amZmZTssUFRWhqKhIUX90dLTUE0JERNWTXq9HUFBQhXyfW+ru9p/n4RXgq2rZ4r+K8N9H3qpS55nz589j9erV+PXXX7F8+XKEhYXhm2++QaNGjZCQkOBSnR6//CEXHByM5s2b49y5c1LazZs30a9fPwQGBmLTpk0lBhSAqffj8uXLiqDBlq+vL3Q6nWIiIiIqi+r6QjG53bt3o02bNjh06BA2btyI/Px8AMD//vc/zJkzx+V6q1RQkZ+fj/PnzyMyMhKAKSrs06cPfHx88NVXX8HPz6/UOjIyMlCvXj34+qqLIomIiMqiJtxSOmPGDCxcuBDp6enw8fGR0nv27ImDBw+6XK9Hx1RMnToVAwYMQExMDLKysjBnzhxotVokJSVJAUVBQQE+++wzxYDK0NBQaLVabNmyBVeuXEHnzp3h5+eH9PR0LF68GFOnTvXkZhERUQ0migJElUGC2vIV7cSJE1i7dq1delhYGP7880+X6/VoUHH58mUkJSXh6tWrCA0NxX333YeDBw8iNDQUP/zwAw4dOgQAiIuLUyx34cIFNG7cGN7e3nj77bcxefJkiKKIuLg4LFu2TNXTv4iIiGqb4OBgZGdno0mTJor048eP46677nK5Xo8GFSU9+fKBBx4o9dbQfv36KR56RUREVNFqwt0fTz/9NKZPn44NGzZAEAQYjUb897//xdSpUzFs2DCX661SYyqIiIiqOsvlD7VTVbJ48WK0bNkS0dHRyM/PR3x8PLp3746uXbti1qxZLtfr8SdqEhERVSeiCz0VVS2o8PHxwYoVK5CamoqTJ08iPz8f7du3R7NmzcpVL4MKIiIiFUQAap/wVGUeCGWjUaNGaNSokdvqY1BBRESkghECBJXPnVD7nAqDwYC5c+fis88+Q05ODqKiovDss89i1qxZEAT7usaOHYv3338fr7/+OiZNmuSwzilTppR5/cuWLVPVXgsGFURERFXMyy+/jHfffRcff/wxEhIS8OOPP2LEiBEICgqyew3Fpk2bcPDgQURFRZVY5/HjxxWfjx07huLiYrRo0QIAcObMGWi1Wtxzzz0ut5tBBRERkQqV8ZyK/fv345FHHkFiYiIAoHHjxvj3v/+Nw4cPK8r9/vvvmDBhAr777juprDO7du2S5pctW4bAwEB8/PHHqFevHgDg+vXrGDFiBO6//35VbZXj3R9EREQqlOeJmrZvyHb2SomuXbti586dOHPmDADT47P37duH/v37W9thNGLo0KGYNm2a6nd1vPbaa0hLS5MCCgCoV68eFi5ciNdee03tLpGwp4KIiEgFUXRhoKa5fHR0tCJ9zpw5mDt3rl35GTNmQK/Xo2XLltBqtTAYDFi0aBGSk5OlMi+//DK8vLxKfCu3M3q9Hn/88Ydd+h9//IGbN2+qrs+CQQUREZEK5bn8kZmZqXiJpbP3VK1fvx5r1qzB2rVrkZCQgIyMDEyaNAlRUVEYPnw4jh49in/96184duyYw4GbpXnssccwYsQIvPbaa+jYsSMA4NChQ5g2bRoGDRqkuj4LBhVEREQqlCeoKOubsadNm4YZM2bg6aefBgC0adMGFy9eRFpaGoYPH469e/ciNzdXcTuowWDAiy++iOXLl+O3334rsf733nsPU6dOxTPPPIM7d+4AALy8vDBq1Ci88sorqrZNjkEFERFRFVNQUACNRjnsUavVwmg0AgCGDh2K3r17K/L79u2LoUOHYsSIEaXW7+/vj3feeQevvPIKzp8/DwCIjY1FQEBAudrNoIKIiEgFoyhAqOB3fwwYMACLFi1Co0aNkJCQgOPHj2PZsmUYOXIkACAkJAQhISGKZby9vRERESHdIloWAQEBaNu2raq2lYR3fxAREalgGaipdlLjzTffxOOPP47x48ejVatWmDp1KsaMGYMFCxa43O6xY8fi8uXLZSr7+eefY82aNarXwZ4KIiIiFUxBgtoxFerWERgYiOXLl2P58uVlXqa0cRShoaFISEhAt27dMGDAAHTo0AFRUVHw8/PD9evXcerUKezbtw/r1q1DVFQUPvjgA3WNBoMKIiIiVSrj4VcVYcGCBXj++efx4Ycf4p133sGpU6cU+YGBgejduzc++OAD9OvXz6V1MKggIiJSQYT6F4RVlReKhYeH46WXXsJLL72E69ev49KlS7h16xYaNGiA2NhYl25PlWNQQUREVAvVq1dP8URNd2BQQUREpEJ1vfxRGRhUEBERqVGdr39UMAYVREREarjQUwH2VBAREZGt8rxQrKZjUEFERKQCx1Q4x6CCiIioFmjfvn2Zbxk9duyYS+tgUEFERKSGKKgfI1EFeioeffTRCl8HgwoiIiIVquuYijlz5lT4Ojz6QrG5c+dCEATF1LJlSym/sLAQKSkpCAkJQd26dTF48GBcuXJFUcelS5eQmJgIf39/hIWFYdq0aSguLq7sTSEiotpCdHGqBTzeU5GQkIAdO3ZIn728rE2aPHkyvv76a2zYsAFBQUF4/vnnMWjQIPz3v/8FABgMBiQmJiIiIgL79+9HdnY2hg0bBm9vbyxevLjSt4WIiGq+mjBQ02Aw4PXXX8f69etx6dIl3L59W5F/7do1l+r1+KvPvby8EBERIU0NGjQAAOTl5WHlypVYtmwZevbsiXvuuQerV6/G/v37cfDgQQDA9u3bcerUKXz22Wdo164d+vfvjwULFuDtt9+220FERERuU817KebNm4dly5bhqaeeQl5eHqZMmYJBgwZBo9Fg7ty5Ltfr8aDi7NmziIqKQtOmTZGcnIxLly4BAI4ePYo7d+6gd+/eUtmWLVuiUaNGOHDgAADgwIEDaNOmDcLDw6Uyffv2hV6vx88//+x0nUVFRdDr9YqJiIiotlizZg1WrFiBF198EV5eXkhKSsKHH36I2bNnSz/cXeHRoKJTp0746KOP8O233+Ldd9/FhQsXcP/99+PmzZvIycmBj48PgoODFcuEh4cjJycHAJCTk6MIKCz5ljxn0tLSEBQUJE3R0dHu3TAiIqqxLJc/1E5VSU5ODtq0aQMAqFu3LvLy8gAADz/8ML7++muX6/VoUNG/f3888cQTaNu2Lfr27Ytt27bhxo0bWL9+fYWud+bMmcjLy5OmzMzMCl0fERHVIDVgoGbDhg2RnZ0NAIiNjcX27dsBAEeOHIGvr6/L9Xr88odccHAwmjdvjnPnziEiIgK3b9/GjRs3FGWuXLmCiIgIAEBERITd3SCWz5Yyjvj6+kKn0ykmIiKishFcnKqOxx57DDt37gQATJgwAampqWjWrBmGDRuGkSNHulyvx+/+kMvPz8f58+cxdOhQ3HPPPfD29sbOnTsxePBgAMDp06dx6dIldOnSBQDQpUsXLFq0CLm5uQgLCwMApKenQ6fTIT4+3mPbQURENVgNeEvpkiVLpPmnnnoKMTEx2L9/P5o1a4YBAwa4XK9Hg4qpU6diwIABiImJQVZWFubMmQOtVoukpCQEBQVh1KhRmDJlCurXrw+dTocJEyagS5cu6Ny5MwCgT58+iI+Px9ChQ7F06VLk5ORg1qxZSElJKVf3DRERkVM1IKgoLCyEn5+f9Llz587SubU8PBpUXL58GUlJSbh69SpCQ0Nx33334eDBgwgNDQUAvP7669BoNBg8eDCKiorQt29fvPPOO9LyWq0WW7duxbhx49ClSxcEBARg+PDhmD9/vqc2iYiIqMoLCwvDY489hiFDhqBXr17QaNwzGkIQxarw8FDP0uv1CAoKQl5eHsdXEBFVYxX5fW6pO/rtedDU8St9ARnjrUJkpsypMueZTZs2Ye3atfj6668RFBSEp556CkOGDEGHDh3KVW+VGqhJRERU1Vne/aF2qkoee+wxbNiwAVeuXMHixYtx6tQpdO7cGc2bNy9Xb7+qnoobN25g06ZN2Lt3Ly5evIiCggKEhoaiffv26Nu3L7p27epyQzyJPRVERDVDZfRUNHzTtZ6KyxOqTk+FI6dOnUJycjJ++uknGAwGl+ooU09FVlYW/vGPfyAyMhILFy7ErVu30K5dO/Tq1QsNGzbErl278Pe//x3x8fH4/PPPXWoIERFRtWB59bnaqQoqLCzE+vXr8eijj+Luu+/GtWvXMG3aNJfrK9NAzfbt22P48OE4evSo01s1b926hc2bN2P58uXIzMzE1KlTXW4UERFRVSWIpkntMlXJd999h7Vr12Lz5s3w8vLC448/ju3bt6N79+7lqrdMQcWpU6cQEhJSYpk6deogKSlJupuDiIiIqqbHHnsMDz/8MD755BM89NBD8Pb2dku9ZQoqSgsoylueiIio2qgBz6m4cuUKAgMD3V6vS8+pyMrKwr59+5Cbmwuj0ajImzhxolsaRkREVCW5MkaiCoyp0Ov10iBRURRLfEO3q4NJVQcVH330EcaMGQMfHx+EhIRAEKw7ShAEBhVERFSzVdOeinr16iE7OxthYWEIDg5WnL8tRFGEIAgu3/2hOqhITU3F7NmzMXPmTLc9gYuIiKjaqKZBxffff4/69esDAHbt2lUh61AdVBQUFODpp59mQEFERFSN9OjRAwBQXFyM3bt3Y+TIkWjYsKFb16E6Mhg1ahQ2bNjg1kYQERFVG6KLUxXh5eWFV155BcXFxe6vW+0CaWlpePjhh/Htt9+iTZs2drehLFu2zG2NIyIiqnKq6UBNuZ49e2L37t1o3LixW+t1Kaj47rvv0KJFCwCwG6hJRERUk9WEh1/1798fM2bMwIkTJ3DPPfcgICBAkT9w4ECX6lUdVLz22mtYtWoVnn32WZdWSEREVK1V04GacuPHjwfg+OpCpd794evri27durm0MiIiIvI822dMuYvqgZovvPAC3nzzzYpoCxEREVWwO3fuwMvLCydPnnR73ap7Kg4fPozvv/8eW7duRUJCgt1AzS+//NJtjSMiIqpqBLgwpqJCWuIab29vNGrUyOVLHCVRHVQEBwdj0KBBbm8IERFRtVAD7v546aWX8H//93/49NNPpQdiuYPqoGL16tVuWzkREVG1UwMGar711ls4d+4coqKiEBMTY3f3x7Fjx1yq16UXihEREdVaNSCoePTRRyuk3jIFFf369cPcuXPRuXPnEsvdvHkT77zzDurWrYuUlBS3NJCIiKgqqQnPqZgzZ47TvPKMtShTUPHEE09g8ODBCAoKwoABA9ChQwdERUXBz88P169fx6lTp7Bv3z5s27YNiYmJeOWVV1xuEBEREVW+M2fOYOXKlfjkk0+QnZ3tUh1lCipGjRqFIUOGYMOGDfj888/xwQcfIC8vD4DpIRnx8fHo27cvjhw5glatWrnUECIiomqhBlz+sCgoKMDnn3+OVatW4cCBA+jQoQOmTJnicn1lHlPh6+uLIUOGYMiQIQCAvLw83Lp1CyEhIXa3lRIREdVYNSCoOHjwID788ENs2LABjRo1wi+//IJdu3bh/vvvL1e9Lr+/PCgoCBEREW4LKJYsWQJBEDBp0iQAwG+//QZBEBxO8rekOspft26dW9pERERkyzKmQu1UFbz22mtISEjA448/jnr16mHPnj04ceIEBEFASEhIueuvEnd/HDlyBO+//z7atm0rpUVHR9td0/nggw/wyiuvoH///or01atXo1+/ftLn4ODgCm0vERHVYtX4ORXTp0/H9OnTMX/+fGi1WrfX73JPhbvk5+cjOTkZK1asQL169aR0rVaLiIgIxbRp0yY8+eSTqFu3rqKO4OBgRTk/P7/K3gwiIqotRBenKmDBggXYsGEDmjRpgunTp7v9Ud0eDypSUlKQmJiI3r17l1ju6NGjyMjIwKhRoxzW0aBBA3Ts2BGrVq2CKFaRvx4REVEVMnPmTJw5cwaffvopcnJy0KlTJ/ztb3+DKIq4fv16uev3aFCxbt06HDt2DGlpaaWWXblyJVq1aoWuXbsq0ufPn4/169cjPT0dgwcPxvjx40t94VlRURH0er1iIiIiKovKGFNhMBiQmpqKJk2aoE6dOoiNjcWCBQukH8137tzB9OnT0aZNGwQEBCAqKgrDhg1DVlZWmerv0aMHPv74Y+Tk5GD8+PG455570KNHD3Tt2tXh69DLSnVQMXz4cOzZs8flFVpkZmbihRdewJo1a0q9XHHr1i2sXbvWYS9FamoqunXrhvbt22P69On45z//WepzMtLS0hAUFCRN0dHR5doWIiKqRSrh8sfLL7+Md999F2+99RZ++eUXvPzyy1i6dKn0o7mgoADHjh1Damoqjh07hi+//BKnT5/GwIEDVa0nMDAQY8aMwaFDh3D8+HF07NgRS5YsUddYGUFUea3g0UcfxbZt2xATE4MRI0Zg+PDhuOuuu1SvePPmzXjssccUA0UMBgMEQYBGo0FRUZGU9+mnn2LUqFH4/fffERoaWmK9X3/9NR5++GEUFhbC19fXYZmioiIUFRVJn/V6PaKjo5GXlwedTqd6W4iIqGrQ6/UICgqqkO9zS91NUxdDq3LsnqGwEL8u+L8yt+vhhx9GeHg4Vq5cKaUNHjwYderUwWeffeZwmSNHjqBjx464ePEiGjVqpKp9cnfu3HH5zk7VPRWbN2/G77//jnHjxuHzzz9H48aN0b9/f3zxxRe4c+dOmevp1asXTpw4gYyMDGnq0KEDkpOTkZGRoQg2Vq5ciYEDB5YaUABARkYG6tWr5zSgAEzP3NDpdIqJiIioTMrRU2F76V3+A1eua9eu2LlzJ86cOQMA+N///od9+/bZ3f0ol5eXB0EQyn0HZHkeFeHSLaWhoaGYMmUKpkyZgmPHjmH16tUYOnQo6tatiyFDhmD8+PFo1qxZiXUEBgaidevWirSAgACEhIQo0s+dO4c9e/Zg27ZtdnVs2bIFV65cQefOneHn54f09HQsXrwYU6dOdWWziIiISleOh1/ZXm6fM2cO5s6da1d8xowZ0Ov1aNmyJbRaLQwGAxYtWoTk5GSH1RcWFmL69OlISkry6A/lcj2nIjs7G+np6UhPT4dWq8VDDz2EEydOID4+HkuXLsXkyZPL3cBVq1ahYcOG6NOnj12et7c33n77bUyePBmiKCIuLg7Lli3D6NGjy71eIiIid8vMzFSc9J31qq9fvx5r1qzB2rVrkZCQgIyMDEyaNAlRUVEYPny4ouydO3fw5JNPQhRFvPvuuxXa/tKoHlNx584dfPXVV1i9ejW2b9+Otm3b4h//+AeeeeYZaUdt2rQJI0eOdMvtKZWhIq/BERFR5amMMRWx/+famIrzi8s+piI6OhozZsxQvPF74cKF+Oyzz/D//t//k9IsAcWvv/6K77//vsSnYk6ZMgULFixAQEAA9uzZg65du8LLy73PwFRdW2RkJIxGI5KSknD48GG0a9fOrsyDDz7Ip1oSERG5qKCgABqNctijVquF0WiUPlsCirNnz2LXrl2lPmb7zTffxPTp0xEQEIAHH3wQ2dnZCAsLc2u7VQcVr7/+Op544okSbwMNDg7GhQsXytUwIiKiKqkSXig2YMAALFq0CI0aNUJCQgKOHz+OZcuWYeTIkQBMAcXjjz+OY8eOYevWrTAYDMjJyQEA1K9fHz4+PnZ1Nm7cGG+88Qb69OkDURRx4MABxZOs5bp3766uwWaqL3/URLz8QURUM1TG5Y+4Ga5d/ji3pOyXP27evInU1FRs2rQJubm5iIqKQlJSEmbPng0fHx/89ttvaNKkicNld+3ahQceeMAuffPmzRg7dixyc3MhCILTp08LggCDwaBq+6RlGVQwqCAiqikqLajwVRlUFKkLKipSfn4+dDodTp8+7fTyR1BQkEt1V4m3lBIREVHlqFu3Lnbt2oUmTZp4fqAmERFRrVYJYyoqWo8ePWAwGLBx40b88ssvAID4+Hg88sgj5XolOoMKIiIiFVx5QZja8hXt3LlzSExMxOXLl9GiRQsApvdiRUdH4+uvv0ZsbKxL9Xr81edERETVSiW8UKyiTZw4EU2bNkVmZiaOHTuGY8eO4dKlS2jSpAkmTpzocr3sqSAiIlKhJvRU7N69GwcPHkT9+vWltJCQECxZsgTdunVzuV4GFURERGrUgDEVvr6+uHnzpl16fn6+w2dclBUvfxAREdUyDz/8MJ577jkcOnQIoihCFEUcPHgQY8eOxcCBA12ul0EFERGRGjVgTMUbb7yB2NhYdOnSBX5+fvDz80O3bt0QFxeHf/3rXy7Xy8sfREREKtSEMRXBwcH4z3/+g3Pnzkm3lLZq1QpxcXHlqpdBBRERkRo1YEyFRVxcXLkDCTkGFURERGrUoKDC3RhUEBERqVATLn9UFA7UJCIiIrdgTwUREZEavPzhFHsqiIiIVLBc/lA7VTV79+7FkCFD0KVLF/z+++8AgE8//RT79u1zuU4GFURERGrUgOdUbNy4EX379kWdOnVw/PhxFBUVAQDy8vKwePFil+tlUEFERKRGDQgqFi5ciPfeew8rVqyAt7e3lN6tWzccO3bM5Xo5poKIiEgFwTypXaYqOX36NLp3726XHhQUhBs3brhcL3sqiIiIapmIiAicO3fOLn3fvn1o2rSpy/UyqCAiIlKjBlz+GD16NF544QUcOnQIgiAgKysLa9aswdSpUzFu3DiX6+XlDyIiIhVqwsOvZsyYAaPRiF69eqGgoADdu3eHr68vpk6digkTJrhcb5XpqViyZAkEQcCkSZOktAceeACCICimsWPHKpa7dOkSEhMT4e/vj7CwMEybNg3FxcWV3HoiIqo1akBPhSAIeOmll3Dt2jWcPHkSBw8exB9//IEFCxaUq94q0VNx5MgRvP/++2jbtq1d3ujRozF//nzps7+/vzRvMBiQmJiIiIgI7N+/H9nZ2Rg2bBi8vb3LdUsMERFRiapYkOAqHx8fxMfHu60+jwcV+fn5SE5OxooVK7Bw4UK7fH9/f0RERDhcdvv27Th16hR27NiB8PBwtGvXDgsWLMD06dMxd+5c+Pj4VHTziYiolqmulz8GDRpU5rJffvmlS+vw+OWPlJQUJCYmonfv3g7z16xZgwYNGqB169aYOXMmCgoKpLwDBw6gTZs2CA8Pl9L69u0LvV6Pn3/+ucLbTkREVF0EBQWVeXKVR3sq1q1bh2PHjuHIkSMO85955hnExMQgKioKP/30E6ZPn47Tp09LEVROTo4ioAAgfc7JyXG63qKiIunpYQCg1+vLuylERFRbVNN3f6xevbrC1+GxoCIzMxMvvPAC0tPT4efn57DMc889J823adMGkZGR6NWrF86fP4/Y2FiX152WloZ58+a5vDwREdVe1fXyR2XwWFBx9OhR5Obm4u6775bSDAYD9uzZg7feegtFRUXQarWKZTp16gQAOHfuHGJjYxEREYHDhw8ryly5cgUAnI7DAICZM2diypQp0me9Xo/o6OhybxMREdUC1bSnQq59+/YQBPvnfAqCAD8/P8TFxeHZZ5/Fgw8+qKpej42p6NWrF06cOIGMjAxp6tChA5KTk5GRkWEXUABARkYGACAyMhIA0KVLF5w4cQK5ublSmfT0dOh0uhJHs/r6+kKn0ykmIiKisqgJbynt168ffv31VwQEBODBBx/Egw8+iLp16+L8+fO49957kZ2djd69e+M///mPqno91lMRGBiI1q1bK9ICAgIQEhKC1q1b4/z581i7di0eeughhISE4KeffsLkyZPRvXt36dbTPn36ID4+HkOHDsXSpUuRk5ODWbNmISUlBb6+vp7YLCIiqulqQE/Fn3/+iRdffBGpqamK9IULF+LixYvYvn075syZgwULFuCRRx4pc70ev/vDGR8fH+zYsQN9+vRBy5Yt8eKLL2Lw4MHYsmWLVEar1WLr1q3QarXo0qULhgwZgmHDhimea0FERERK69evR1JSkl36008/jfXr1wMAkpKScPr0aVX1evw5FXI//PCDNB8dHY3du3eXukxMTAy2bdtWga0iIiKSqQE9FX5+fti/fz/i4uIU6fv375dunjAajU5vpHCmSgUVREREVV1NuPtjwoQJGDt2LI4ePYp7770XgOnp1h9++CH+7//+DwDw3XffoV27dqrqZVBBRESkRg3oqZg1axaaNGmCt956C59++ikAoEWLFlixYgWeeeYZAMDYsWNVv7GUQQUREZEKgihCENVFCWrLV4bk5GQkJyc7za9Tp47qOhlUEBERqVEDeiosbt++jdzcXBiNRkV6o0aNXKqPQQUREVEtc/bsWYwcORL79+9XpIuiCEEQYDAYXKqXQQUREZEKNWGg5rPPPgsvLy9s3boVkZGRDp+u6QoGFURERGrUgMsfGRkZOHr0KFq2bOnWehlUEBERqVATeiri4+Px559/ur3eKvtETSIioipJdHGqQl5++WX885//xA8//ICrV69Cr9crJlexp4KIiEiFmtBT0bt3bwCml3vKcaAmERERqbJr164KqZdBBRERkRo1YKBmjx49nOadPHnS5Xo5poKIiEglyyWQsk5V3c2bN/HBBx+gY8eO+Nvf/uZyPQwqiIiI1BBF16YqaM+ePRg+fDgiIyPx6quvomfPnjh48KDL9fHyBxERkQrVfaBmTk4OPvroI6xcuRJ6vR5PPvkkioqKsHnzZsTHx5erbvZUEBER1RIDBgxAixYt8NNPP2H58uXIysrCm2++6bb62VNBRESkRjUeqPnNN99g4sSJGDduHJo1a+b2+tlTQUREpIJgdG2qCvbt24ebN2/innvuQadOnfDWW2+59cmaDCqIiIjUqIQnahoMBqSmpqJJkyaoU6cOYmNjsWDBAoiyAZ+iKGL27NmIjIxEnTp10Lt3b5w9e7bEejt37owVK1YgOzsbY8aMwbp16xAVFQWj0Yj09HTcvHlTXUNtMKggIiJSQe3tpK4M7Hz55Zfx7rvv4q233sIvv/yCl19+GUuXLlWMf1i6dCneeOMNvPfeezh06BACAgLQt29fFBYWllp/QEAARo4ciX379uHEiRN48cUXsWTJEoSFhWHgwIFqd4mEQQUREZEalXBL6f79+/HII48gMTERjRs3xuOPP44+ffrg8OHD5iaIWL58OWbNmoVHHnkEbdu2xSeffIKsrCxs3rxZ1bpatGiBpUuX4vLly/j3v/+tallbDCqIiIgqie2Lu4qKihyW69q1K3bu3IkzZ84AAP73v/9h37596N+/PwDgwoULyMnJkd7hAQBBQUHo1KkTDhw44FLbtFotHn30UXz11VcuLQ/w7g8iIiJVyvOciujoaEX6nDlzMHfuXLvyM2bMgF6vR8uWLaHVamEwGLBo0SIkJycDMD1rAgDCw8MVy4WHh0t5nsCggoiISI1y3FKamZkJnU4nJfv6+josvn79eqxZswZr165FQkICMjIyMGnSJERFRWH48OGutbsSMKggIiJSoTw9FTqdThFUODNt2jTMmDEDTz/9NACgTZs2uHjxItLS0jB8+HBEREQAAK5cuYLIyEhpuStXrqBdu3bqGudGVWZMxZIlSyAIAiZNmgQAuHbtGiZMmIAWLVqgTp06aNSoESZOnIi8vDzFcoIg2E3r1q3zwBYQEVGtUAkDNQsKCqDRKE/RWq0WRqPpgRdNmjRBREQEdu7cKeXr9XocOnQIXbp0Kf82uqhK9FQcOXIE77//Ptq2bSulZWVlISsrC6+++iri4+Nx8eJFjB07FllZWfjiiy8Uy69evRr9+vWTPgcHB1dW04mIqJapjHd/DBgwAIsWLUKjRo2QkJCA48ePY9myZRg5cqSpPvOP8IULF6JZs2Zo0qQJUlNTERUVhUcffVTdytzI40FFfn4+kpOTsWLFCixcuFBKb926NTZu3Ch9jo2NxaJFizBkyBAUFxfDy8va9ODgYKkriIiIqLp78803kZqaivHjxyM3NxdRUVEYM2YMZs+eLZX55z//ib/++gvPPfccbty4gfvuuw/ffvst/Pz8PNZuj1/+SElJQWJiouK2GGfy8vKg0+kUAYWljgYNGqBjx45YtWqV4oljREREblUJT9QMDAzE8uXLcfHiRdy6dQvnz5/HwoUL4ePjI5URBAHz589HTk4OCgsLsWPHDjRv3rz821cOHu2pWLduHY4dO4YjR46UWvbPP//EggUL8NxzzynS58+fj549e8Lf3x/bt2/H+PHjkZ+fj4kTJzqtq6ioSHFvsF6vd30jiIioVqnurz6vSB4LKjIzM/HCCy8gPT291K4avV6PxMRExMfH293Pm5qaKs23b98ef/31F1555ZUSg4q0tDTMmzevXO0nIqJayiiaJrXL1AIeu/xx9OhR5Obm4u6774aXlxe8vLywe/duvPHGG/Dy8oLBYAAA3Lx5E/369UNgYCA2bdoEb2/vEuvt1KkTLl++7PQpZQAwc+ZM5OXlSVNmZqZbt42IiGqwSrj8UV15rKeiV69eOHHihCJtxIgRaNmyJaZPnw6tVgu9Xo++ffvC19cXX331VZkGn2RkZKBevXpOHygCmB42UlI+ERGRMwJcuPxRIS2pejwWVAQGBqJ169aKtICAAISEhKB169bQ6/Xo06cPCgoK8Nlnn0nPSQeA0NBQaLVabNmyBVeuXEHnzp3h5+eH9PR0LF68GFOnTvXEJhEREdVqHr+l1Jljx47h0KFDAIC4uDhF3oULF9C4cWN4e3vj7bffxuTJkyGKIuLi4rBs2TKMHj3aE00mIqLawIWHWakuX01VqaDihx9+kOYfeOCBUm8N7devn+KhV0RERBWNd384V6WCCiIioiqvHC8Uq+kYVBAREakgiCIElZcz1JavrhhUEBERqWE0T2qXqQU8/phuIiIiqhnYU0FERKQCL384x6CCiIhIDQ7UdIpBBRERkRp8ToVTDCqIiIhU4HMqnGNQQUREpAZ7Kpzi3R9ERETkFuypICIiUkEwmia1y9QGDCqIiIjU4OUPpxhUEBERqcFbSp1iUEFERKQCH37lHIMKIiIiNXj5wyne/UFERERuwZ4KIiIiNUSof+to7eioYFBBRESkBsdUOMeggoiISA0RLoypqJCWVDkMKoiIiNTgQE2nGFQQERGpYQQguLBMLcC7P4iIiMgt2FNBRESkAgdqOseggoiISA2OqXCKQQUREZEaDCqcqjJjKpYsWQJBEDBp0iQprbCwECkpKQgJCUHdunUxePBgXLlyRbHcpUuXkJiYCH9/f4SFhWHatGkoLi6u5NYTEVGtYQkq1E61QJUIKo4cOYL3338fbdu2VaRPnjwZW7ZswYYNG7B7925kZWVh0KBBUr7BYEBiYiJu376N/fv34+OPP8ZHH32E2bNnV/YmEBFRbWF0caoFPB5U5OfnIzk5GStWrEC9evWk9Ly8PKxcuRLLli1Dz549cc8992D16tXYv38/Dh48CADYvn07Tp06hc8++wzt2rVD//79sWDBArz99tu4ffu2pzaJiIioVvJ4UJGSkoLExET07t1bkX706FHcuXNHkd6yZUs0atQIBw4cAAAcOHAAbdq0QXh4uFSmb9++0Ov1+Pnnn52us6ioCHq9XjERERGVheXuD7VTbeDRgZrr1q3DsWPHcOTIEbu8nJwc+Pj4IDg4WJEeHh6OnJwcqYw8oLDkW/KcSUtLw7x588rZeiIiqpU4UNMpj/VUZGZm4oUXXsCaNWvg5+dXqeueOXMm8vLypCkzM7NS109ERNWYUXRtqgU8FlQcPXoUubm5uPvuu+Hl5QUvLy/s3r0bb7zxBry8vBAeHo7bt2/jxo0biuWuXLmCiIgIAEBERITd3SCWz5Yyjvj6+kKn0ykmIiKiMuHdH055LKjo1asXTpw4gYyMDGnq0KEDkpOTpXlvb2/s3LlTWub06dO4dOkSunTpAgDo0qULTpw4gdzcXKlMeno6dDod4uPjK32biIioNnAloKgdQYXHxlQEBgaidevWirSAgACEhIRI6aNGjcKUKVNQv3596HQ6TJgwAV26dEHnzp0BAH369EF8fDyGDh2KpUuXIicnB7NmzUJKSgp8fX0rfZuIiIhqsyr9RM3XX38dGo0GgwcPRlFREfr27Yt33nlHytdqtdi6dSvGjRuHLl26ICAgAMOHD8f8+fM92GoiIqrROFDTKUEUa8mWlkCv1yMoKAh5eXkcX0FEVI1V5Pe5pe7eMc/DS6OuN7zYWIQdF9+q8eeZKt1TQUREVOWIRtOkdplagEEFERGRGrz84RSDCiIiIjWMLtzNwedUEBEREZUdeyqIiIjU4OUPpxhUEBERqSHChaCiQlpS5fDyBxERkRqV8Jjuxo0bQxAEuyklJQWA6aWZQ4cORUREBAICAnD33Xdj48aNFbG1qrCngoiISA2jEYDKW0SN6sofOXIEBoNB+nzy5En8/e9/xxNPPAEAGDZsGG7cuIGvvvoKDRo0wNq1a/Hkk0/ixx9/RPv27dW1zY3YU0FERKRGJfRUhIaGIiIiQpq2bt2K2NhY9OjRAwCwf/9+TJgwAR07dkTTpk0xa9YsBAcH4+jRoxWxxWXGoIKIiKgKu337Nj777DOMHDkSgiAAALp27YrPP/8c165dg9FoxLp161BYWIgHHnjAo23l5Y8yuHw2W5q/8tsfiGgcCggCrlz8A+Exoab0S38ivFEDwPwHN6VdRXijENN85lVTvmU+OgSQigq4cvkawhvWN+Vfvobw6PqQFcCV368j/K56gCCbB3Al6wbCo+pZGytY0oIBADnZNxARFQzRXFfD6Pq4nHlNKpuTk4eIiCBTWcu8dbXIuZKHiPAgxbwoCMjJzUNEWJBiP2Xn5iEyzLS8NC/P/8O6TPYfeYgMNc1n/ZmHqAbK9Wb9qUdUA9OjbLOu6hEVogME03xkiPIRt1nX9Iiqb863zMv8fk2Pu8xpv19XzkfVk5UVgN9v6HFXsDlfNi/VdUOPu8zLXM7To2GQcl6UbcPlvDw0DDJt42V9HhrqTNsozctcvpmHhoHmfMu8RFTm5+ehYV1zvbJ5qa6/bqBh3WBpPjogGJZRYo0DQ/Bb/lXr9hTcwF3+wRAAZBVcR5S/9VgSBBFZt64jqo4pLfvWdUTWqQcBQPata4iUlwWQU3gNEX71IQC4UngN4eayFrmFVxHmFwIBwB9FVxHqWx+CAPxR9CdCfRvI6hJxtehPhPg2gADgatEfCPENhSCIuHb7D9T3CVWUvX47F/V9wgAAN25fQT3zvGkbAP3tHOh8IiBAhP5ODnTepvmbd7IR6B0JQTaC7q87WajrHQUBIgruZMHfJ0q2DSIKi39HHa+7AACFxZdRx6shBIgoKr4MX6+GirJ3ijPh4xUNANZ5ASguzoS3Od2y74zFl6HxipbNN4TGqwmoiirH3R96vV6R7OvrW+oLMDdv3owbN27g2WefldLWr1+Pp556CiEhIfDy8oK/vz82bdqEuLg4de1yMwYVpbh8Nhv/SJhkOobMXxmCVgNBo4UoihA0GkCrAURLusZ07Gg1pnyt1pwmQtBqAEEDEablBK25rGD6WhM0AqDRSvOO8kWNeV4QAGleA2gEQABEwZovldWYygIiHuzTGt9/fwqAqaxRBASNaR1GS1mtqaw8XxQgawOU9YoijPJ8wXS1URDMZSDCqIFUv1SXAIgaSOuQylraIgCQ5SvmBUsbLfUCkK23xHybstb1KvNFWb4oioAAGABozPkGmDffvG0ac3tFiFL9tvmiIMrKCuayomJ5I0T7fNG0XkFjnZenawSYjytlWVEUYT1ERTwc3Rrbfj9pKiuYvhg1GkCjMc3LDiUIghEwt0UriNK8RmNN1wimfaSBASJEaAXAS2O0zgumL1KNYEkT4CUYIcJomtcYTPMQTNsMEd6yNC+NAYARGvO8ACM0ggANRAgQ4SWY8wUB3oIBAgzmeUsbjaZlIMBbKJbK+ljmYVoOALSC0bS8uaxlXV6CAaY/jQFeKIYgaKCFCAHFEKCBj3m9gqCBDyzrFeFtzteY948ADbwFmNuugbegMZWFAC8AAjTQCoJp30ML39B0BhZVVTkefhUdHa1InjNnDubOnVvioitXrkT//v0RFRUlpaWmpuLGjRvYsWMHGjRogM2bN+PJJ5/E3r170aZNG3VtcyMGFaXY++VBGIotA2zMJ/JiIwSt6WQuGoymCFSrhWgwQjCK5rOlaAosDAaIRlNAIRqMgGD6xhcNRilgAERTvQbRdPY2z9vmGw2i6QxlOcmJIkSNBqJoNC0nCBA1pvWb3hNnDiyMopT//Y6fYRRNAYNUrxHmswhgNJqXk/LN44sEQcoXYfpsaq+pLvPqTO22BCkiYPmHJ5p3n1GWLwX7lnWYT9yiYD4pizDvD0u+rKxgChiUaZACD2m9DvItuwvmP5NoDj4sZW3zLcEJRMs+suYbLOuS5mXLOcrXyNKNpoBBqteuLpt885eSYJkXTPvBaASMgqlnAUZZPkQIAmAw/80EAdiaeRJGc0AhiNZ8S1nRKJoPFdF08hMAg2gNKAyi6XiXp5v3oJQviLJ5mOoSzWlG0QCDVNYAQTSa5mH+dySIMEhppnytABhhgNEyLxpMdUKE0VzWKBpghEHKN22jEUbRFNyI5uVNMbABRpiXM6cLgijVL9qsS4QRMAdCpmPKAFEwmn9iFEMUjeZ/psUQBVOwA9FoDkyNUkBqmjcfHzCYl7P0k2jMaYLpRwKKYSj4Bhrd+NK/pKjSiaLR9L2rchkAyMzMVLxQrLReiosXL2LHjh348ssvpbTz58/jrbfewsmTJ5GQkAAA+Nvf/oa9e/fi7bffxnvvvaeqbe7EoKIUZ348b5MiyLrpzQGEJYiwzAM2aXCcL6tSkSbK0iCLh23yRdt12BBlZS2zokE0/yQ2p8vX5aAOaV2KfEGRrSjuqB2O6pVXZbO9Jebbli0hX3SSL2+PXdsc1W+TLzrId/qbxbYtihXZlhVLyBeVh55tmZL2say8JeYtiW2+s11vWb88X1NCWcFSWHD0JzMFFJZTrLO6lIerKNVV8npFmKKtkg8lU51Gh2XhYBkBRkUbSipbchtNYZlg6Qm11H77JKiKEkX1j902X/7Q6XSq3lK6evVqhIWFITExUUorKCgAAGg0ymGRWq0WRpV3mbgbg4pSyU/+ppO4oNHIvnmtgYIgDxhk85YvC/tva5uzuW3QoQhQIH0hA5ZfyvJ6BUWaaFlesKlLYz0pipazlADZr37Bmi9vnpRv2wb7E6Z8OVFev1SXYFdOfrKxpgvWumV1WOoE4DDfjk39UppsXr4NdvMyls/WdYnK7YDt/hCVQYyz9tnmK9on2s/L9oHpZGxJs+bLdqNyXZbyinxLL4hol2bpjbAvaypvPvpk7ZYHG9Z5IwCtrD5BkNVrPvmbDllrD4hU1ibYsB5GluXMPTcwmucdr1cKMKR6jYp9bddGqaxtXfJAQZS12bYuxyce20NVXq8RIrRlixLJU0T7v3fZllHHaDRi9erVGD58OLy8rKfrli1bIi4uDmPGjMGrr76KkJAQbN68Genp6di6davq9bgTg4rSmC5OA4D52jtMgYI5zXyR2fylLpgvdkPqDZDGO9jmK070lnybspaTqixNtMkHzAGEJWCVTsTWs4riJCyv1ybNWtZRvnVeETSYz15OgwbbE67t8g7aaN0+ONwGeX2Ot9Fm3mZddu2V2l3yvF0Q5Gh5R+l2+aKyrPyzw3xHddt8QQmy/1iCC0fBiKXHQDqpQzqZS0UtJ2v7Q02ZLqvLGp/KlxOluhT5ikDCdvMsJ3NrWVGq1yjbFmsdUj6MtrG3zTpMZbVS4GINCqwBhLWdomXbpT0j34dGKcyR1ytvv7Q/ofxNYN8ZJjicJ9qxYwcuXbqEkSNHKtK9vb2xbds2zJgxAwMGDEB+fj7i4uLw8ccf46GHHvJQa00YVJRCEARZMKExfTlqtIoTvdRDYenBEExlBUAKOhT5UrrsWxuCddQbLIGCPB+KfOu3nWD+6SY7kZvTRJuzgt2vYOvZwTTOwZxmPdHb5su+YS0nZgdBg5QmG/CobLOD5SEr6ygokbZNvi6b5QG7+qSyiu123AZFr4dtXbBZxtLt7WwdAKRxH/J8eTBguz3mZZT5opQvyOYdBT2CfB7WNEHWk2ENKGSBgk3wYPnlb1mfPFiQBxyKumT5gizfMq9RbIc14JDHjoqONctysnyNPE+aAI25rdZ/BtbeDVN5o1RWkLdBsQtFaV1QrMu6Pg2gWK9iect+ke93eV02kzx80Mg+CZD1bFLVZTSNs1FF5RgMAOjTp495fJy9Zs2aVYknaNpiUFEaAbDrqZCd3AXT0HlzWfO3IARFr4bTSxrSvHV520BAmoe1F8FZWXmvhl3vg13QYBM82J3IZd+Kdm2QpQnySxbWz3YnXCe9F/a9B7KeDCjzHZ1InfY+CDYnedtyim1z8N/S6nK2vDTvPHhwti0l5Qu2+bJ1yE/2Uk+C1Esgr0+UpVvLagRrr4VdDwWsJ3jYzkuHiKPeC/lmyIIT2bosdUvtkgczUl3yyyrmdUGUAgnTgE3rPwspmJDqMn2RW4MT6zgImOc1iuWN1nWax1coeyvkQYJ9j4T8n6jUbPlnQR5OCDbLCzZl5blUpVTS5Y/qiEFFaQSNXU8FBMEUTADKk7/GfFnE5uRvN0l1KS+VSOUtaZaTrCwgUPRkCLCvVyoHu5O/dMKWLm9IZwKbXgBBGRDIB3YKTgIES0AhQPlINcF6icTC4UBHy7evk5Oso3EatnXIWceNWNstBQew3V4HPQ629QpwGuwo1uEkTd67oWy3KFu/PF/5y16RLltesek2+0V+8jZ9tpm32W+OeyKs7VT0LEjbI7tUIT/MzWl2+bIeA0tAoTxBy/OhKGsNNqxtkwcElh4ZeY+Doq0w3bFh23NgvZRitFu38vKIbOyKLN/0X3mAIdrUL69L2S754EyNPA8Cn0xYhYlGI0SVPRVq7xaprhhUlEYQ7E/+issY1nEQduMnbMtKPRkO5uHgkodtXVLPgGB/ycPcRHnvhfzyhTyQkHoEFMGKbHnLvOySh/OxDzZBg8bmhCtvo9QWOE6Tn4xt1yVrg3W9jss6uizjrA3OgpgS67IdLGnTLkXw4KDdtv8tKd/S62DfVpvBmbI/NQTlIaPotQBgO5ZCkOUrxklAdkKXllee3DWWtiiWt7ZXypdtliDbHstnqR3y9snHTgjWQMdal3wgp31AYO1xsPRuWNOt5a2DN63LWwZzyv8UtsGOZRtM6xdl5QBlkCFIqVbKAENQ5IsAB2pWdeypcIpBRWlsT+6CeQyFoqdCNn5CHkzIloFdvnVedJSv6JGwCRQE2bztOpwFGuazjXQJRfp2lg+cVHwjQxFQSN9xtgMtZeuzFlFul+1JWpZmt244yZfVaxtQOKwbzgIC570etmMi5D0W1nlRUc5pOxTnBJsufJtAQApCHLRVIjipx/Ify8nQ0WUX6aOoOGEru+lFKRixHmL2gYV9T4TsRC0/fATrOAr5ydsaOEDxWXZTkfLkbf6vcjyD8k4R66FuvQtDfleKctfKekdkD/SylNEo6rb55wBHAYi87cp1wW552OVb0wTzPEdUUPXGoKIUgqxHQHAy4NJu/ITtSV7xDekg2LDpiQAE6QFR8nzFIEjF8qbPihOcxklPheKWUWu+oqxGsDtZOg4QBOsJVwpg7C9lWMdcWOqylpW2DfJ6YeXgRO3s7g5F+2zqsAswHKzDWeBhXcZRj4FNvu16ofzsMF+RJvsVLwC2vR7ycRSWSxLWgEJ+yCgHZ8p/CwuCfB6yOhxskuXELf35lT0RUmAgX6/gIF9x6UI5ZsK0DtOtm9ajQTYgUmqbaLMO25O/7VgMoyJgkPIF67xGVq+8x8E0WcZXWOu17ZGwXAKybJv80gtkdVnnlSMqlGUFqYzyL0FVjtH6dy8z9lSQicZ+/ISs98I0hkJ28peXldIs86XfMmo9oSuDBsUlD8slDLueCNnJ37ycpV7FIEjbMjbffHbPqZDV5ShosD8JC/YncnndpZzQHQUPaoIGu4AI9tvjsFdDvlxJ9dq2z8l6Tf+1CTQc9UqUsA8cnV+kIEBKs6xDHnAAiiAIssMXNid/WAMHQVGHfHnlOAtLT4Qi2JAfEoLl5C7bJHn9ijSbdsl+8VsDBGd3ephO/hBEmwDCug1SgCFfj2LXKgMQS53ydgGO7+Kw/5PZb59lWdsxFcq7PqhaEZWXyMq+TM3HoKJUsm9v2TMoFM+pkOfb9j5I+ULpt4w6uaQhfzaF/FKJsqws8LDUL5ja7zAIMCcpy9r0PsjSXAoaHKxLGYDY1Cs46NWQ59l889r/ylfWBZu6Sg1Q5MvY1VvSOAn7yxIO22bbLku9Ur58bIR8fU4uadgfPopLFlKadPK3XoKQqjAvY3/YObvjw1lPhHwzRWXdsFlGlqY40cv/yUg9INZLFfY9Bda7OxQDOR08x8L6bArzeApBNg/rNtumyS+3mFZsfe+J7eEj//PIt8dSszJyEGRzppbJ7/bgQM2qzfQ4e3VBgrNbQ2saBhWl0cBmfARg1+NQ4gOtBEh3jZg/Sz0R8jrh4C4PxUlXkE2WNPP/CbLAQDAHF7aBhEb2NSsPIMzFFIMyLWnyOuRpJQYNgsMeAftejxLqldXt9Btb0UabdpRWTn5yt63bpt1OAw2bddn2WigCEWk5214LB9sISDGoXbscPDnTNoZVpNnWafNfuyta0uEi67WQDjknPRE2h7HdOAvzdiuWkf2at5zIrQGP3eElBRS2d3UAon3vg7wdsvVC3g7zJ2vvgfyyiXy/KOuQ/VNStNv2FlPbQ1bZqwKbNlnyBXO+7SUQqpJE8+PcVS9T8zGoKEWAzt/6DSB/BoXDb2ebeQC2AYjDQZnOnmkhDzIAB5c8BNm8LF1alzlN3kMijSoTbL79ZOsQSk5zPD5CflaDdTnLZ0Wa4DSQsBtb4aAux5cZ7Oftytl+2ztsm22eg3ESinUpH8NtrcdB74Jiu2zrVY6JkNWmbKfs5GTtIZEFGLJ2QfYr35Iv77WQLmnIykKeLztBK3eBbX2OxlnYnkAdXfaQBxiWdSjHVjgKQBQ9GIpy1p4GZU+C0b5NNu21D1Dse0WsJxFruzSybYNt3TYs/3zlAYSjMtLzKjSBDushz2NPhXMMKkpxV2y4zfgJKE/+tr0UJTwl0+4pmtZ3TFt7LzSW5WBdzpLv4Imaol1Z0/8p7vIwp4sANBrz20PlJ1Ob51BI65OlKQdympeT6raux+ltojZpFk4HWso5WK60eu3aI1/Gdp1lWJdd+TK20S7QcVivbQDiYNyF7MRuObQEWR02f3775prr0Jhfpy4/hBXNVxxesoGgspOto2BDsF1Gtk7Y1WstJ19Gfsun6VC37bmwv1PEGjhZ862bZQoEZIcs5ANCLXUr6jM/8MpufIZN3Yp9BcU/C+X+lH22ZcoXZJ/N/7NU7NXYwVJEVRuDClgjSL1eb5f3tweawbCo2PzJfFayPLjKcobSADAA0Fo6VEVI1xtEmwc3mG60M32DWn4ACRrpmqria9py8jb/9hEMyjs6pHRRdulElAcgliaa00Sg+30t8f3e06YMrTnd/CpvU1Pkl0eUaaZ6rZtm+80qv2xi2U3O6pKWMdclyE64ouzbWVEXlMsp1it/JLjsG15aXr4uOLgcYlnOYV2iVJej9dveZmptl6g8Y2msZQVzvVLbzCdWU70iIJiutJu23XSgiBAgPUFSA1geviMIgFHjqNdC1qNgzu8dFY/tOSfNAabppCsKljdZWE/mpu0xnYC1ljaZT7YwL2eq2LJvbPMtYxlMbdRqTCdrIwQYYQAgwktjSrHMi9Kvf1NZa77pleaWdWgFQAtL20xltdDAW7AGBBpzr4IlzbqMAV4CcAcAUAwvAF6WNgoiAAO8BeCOuawGGvgIBtlhYYBWEHBbgHm9li9RI7wE68vIvMz7RQNAKwgARHhDkN4PooFGWs6S7wXB3AYRGggQfLpCcPCdRCWzfI9XZM9AsVgEtZczis1HXU0niLWlT6YEly9fRnR0tKebQUREbpKZmYmGDRu6tc7CwkI0adIEOTk5Li0fERGBCxcuwM/Pz63tqkoYVMD0etmsrCwEBgaitj9vX6/XIzo6GpmZmdDpdJ5uTrXCfec67jvXcd8piaKImzdvIioqChqNpvQFVCosLMTt27ddWtbHx6dGBxQAL38AADQajdsj2upOp9PxC8pF3Heu475zHfedVVBQUIXV7efnV+MDg/LgrdBERETkFgwqiIiIyC0YVJCCr68v5syZA19fX083pdrhvnMd953ruO+oKuFATSIiInIL9lQQERGRWzCoICIiIrdgUEFERERuwaCCiIiI3IJBRS2QlpaGe++9F4GBgQgLC8Ojjz6K06dPK8o88MADEARBMY0dO1ZR5tKlS0hMTIS/vz/CwsIwbdo0FBcXoyYry74DgAMHDqBnz54ICAiATqdD9+7dcevWLSn/2rVrSE5Ohk6nQ3BwMEaNGoX8/PzK3JRKV9q+++233+yOOcu0YcMGqRyPO8fHXU5ODoYOHYqIiAgEBATg7rvvxsaNGxVlauNxR57FoKIW2L17N1JSUnDw4EGkp6fjzp076NOnD/766y9FudGjRyM7O1uali5dKuUZDAYkJibi9u3b2L9/Pz7++GN89NFHmD17dmVvTqUqy747cOAA+vXrhz59+uDw4cM4cuQInn/+ecUjgpOTk/Hzzz8jPT0dW7duxZ49e/Dcc895YpMqTWn7Ljo6WnG8ZWdnY968eahbty769+8PgMddScfdsGHDcPr0aXz11Vc4ceIEBg0ahCeffBLHjx+XytTG4448TKRaJzc3VwQg7t69W0rr0aOH+MILLzhdZtu2baJGoxFzcnKktHfffVfU6XRiUVFRRTa3SnG07zp16iTOmjXL6TKnTp0SAYhHjhyR0r755htREATx999/r9D2ViWO9p2tdu3aiSNHjpQ+87gzcbTvAgICxE8++URRrn79+uKKFStEUeRxR57BnopaKC8vDwBQv359RfqaNWvQoEEDtG7dGjNnzkRBQYGUd+DAAbRp0wbh4eFSWt++faHX6/Hzzz9XTsOrANt9l5ubi0OHDiEsLAxdu3ZFeHg4evTogX379knLHDhwAMHBwejQoYOU1rt3b2g0Ghw6dKhyN8CDnB13FkePHkVGRgZGjRolpfG4M3G077p27YrPP/8c165dg9FoxLp161BYWIgHHngAAI878gy+UKyWMRqNmDRpErp164bWrVtL6c888wxiYmIQFRWFn376CdOnT8fp06fx5ZdfAjBdv5V/sQOQPrv6GuDqxtG++/XXXwEAc+fOxauvvop27drhk08+Qa9evXDy5Ek0a9YMOTk5CAsLU9Tl5eWF+vXr1+p9Z2vlypVo1aoVunbtKqXxuHO+79avX4+nnnoKISEh8PLygr+/PzZt2oS4uDgA4HFHHsGgopZJSUnByZMnFb+kASius7Zp0waRkZHo1asXzp8/j9jY2MpuZpXkaN8ZjUYAwJgxYzBixAgAQPv27bFz506sWrUKaWlpHmlrVePsuLO4desW1q5di9TU1EpuWdXnbN+lpqbixo0b2LFjBxo0aIDNmzfjySefxN69e9GmTRsPtZZqOwYVtcjzzz8vDdYq7VXvnTp1AgCcO3cOsbGxiIiIwOHDhxVlrly5AgCIiIiomAZXIc72XWRkJAAgPj5eUb5Vq1a4dOkSANP+yc3NVeQXFxfj2rVrtXrfyX3xxRcoKCjAsGHDFOk87hzvu/Pnz+Ott97CyZMnkZCQAAD429/+hr179+Ltt9/Ge++9V+uPO/IMjqmoBURRxPPPP49Nmzbh+++/R5MmTUpdJiMjA4D1pNmlSxecOHFC8SWVnp4OnU5nd0KtSUrbd40bN0ZUVJTd7X5nzpxBTEwMANO+u3HjBo4ePSrlf//99zAajVLwVhOpOe5WrlyJgQMHIjQ0VJHO487xvrOMd5LfYQQAWq1W6j2rrccdeZhnx4lSZRg3bpwYFBQk/vDDD2J2drY0FRQUiKIoiufOnRPnz58v/vjjj+KFCxfE//znP2LTpk3F7t27S3UUFxeLrVu3Fvv06SNmZGSI3377rRgaGirOnDnTU5tVKUrbd6Ioiq+//rqo0+nEDRs2iGfPnhVnzZol+vn5iefOnZPK9OvXT2zfvr146NAhcd++fWKzZs3EpKQkT2xSpSnLvhNFUTx79qwoCIL4zTff2NXB487xvrt9+7YYFxcn3n///eKhQ4fEc+fOia+++qooCIL49ddfS/XUxuOOPItBRS0AwOG0evVqURRF8dKlS2L37t3F+vXri76+vmJcXJw4bdo0MS8vT1HPb7/9Jvbv31+sU6eO2KBBA/HFF18U79y544Etqjyl7TuLtLQ0sWHDhqK/v7/YpUsXce/evYr8q1eviklJSWLdunVFnU4njhgxQrx582YlbknlK+u+mzlzphgdHS0aDAaH9fC4c7zvzpw5Iw4aNEgMCwsT/f39xbZt29rdYlobjzvyLL76nIiIiNyCYyqIiIjILRhUEBERkVswqCAiIiK3YFBBREREbsGggoiIiNyCQQURERG5BYMKIiIicgsGFUREROQWDCqIarCVK1eiT58+5arjzz//RFhYGC5fvuymVhFRTcUnahLVUIWFhWjatCk2bNiAbt26lauuqVOn4vr161i5cqWbWkdENRF7KohqqC+++AI6na7cAQUAjBgxAmvWrMG1a9fc0DIiqqkYVBBVcX/88QciIiKwePFiKW3//v3w8fHBzp07nS63bt06DBgwQJH27LPP4tFHH8XixYsRHh6O4OBgzJ8/H8XFxZg2bRrq16+Phg0bYvXq1YrlEhISEBUVhU2bNrl344ioRmFQQVTFhYaGYtWqVZg7dy5+/PFH3Lx5E0OHDsXzzz+PXr16OV1u37596NChg136999/j6ysLOzZswfLli3DnDlz8PDDD6NevXo4dOgQxo4dizFjxtiNoejYsSP27t3r9u0jopqDYyqIqomUlBTs2LEDHTp0wIkTJ3DkyBH4+vo6LHvjxg3Uq1cPe/bswf333y+lP/vss/jhhx/w66+/QqMx/aZo2bIlwsLCsGfPHgCAwWBAUFAQPvzwQzz99NPSslOmTMHx48exa9euCtxKIqrOvDzdACIqm1dffRWtW7fGhg0bcPToUacBBQDcunULAODn52eXl5CQIAUUABAeHo7WrVtLn7VaLUJCQpCbm6tYrk6dOigoKCjvZhBRDcbLH0TVxPnz55GVlQWj0YjffvutxLIhISEQBAHXr1+3y/P29lZ8FgTBYZrRaFSkXbt2DaGhoa41nohqBQYVRNXA7du3MWTIEDz11FNYsGAB/vGPf9j1JMj5+PggPj4ep06dclsbTp48ifbt27utPiKqeRhUEFUDL730EvLy8vDGG29g+vTpaN68OUaOHFniMn379sW+ffvcsv6CggIcPXq03A/SIqKajUEFURX3ww8/YPny5fj000+h0+mg0Wjw6aefYu/evXj33XedLjdq1Chs27YNeXl55W7Df/7zHzRq1Egx6JOIyBbv/iCqwZ544gncfffdmDlzZrnq6dy5MyZOnIhnnnnGTS0jopqIPRVENdgrr7yCunXrlquOP//8E4MGDUJSUpKbWkVENRV7KoiIiMgt2FNBREREbsGggoiIiNyCQQURERG5BYMKIiIicgsGFUREROQWDCqIiIjILRhUEBERkVswqCAiIiK3YFBBREREbvH/Ac9PQu4sqQDHAAAAAElFTkSuQmCC\n" }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} } ], "source": [ - "plt.figure(figsize=(12,8))\n", - "\n", - "## User locations\n", - "active_bs_idx = 0 # Select the first active basestation in the dataset\n", - "plt.scatter(DeepMIMO_dataset[active_bs_idx]['user']['location'][:, 1], # y-axis location of the users\n", - " DeepMIMO_dataset[active_bs_idx]['user']['location'][:, 0], # x-axis location of the users\n", - " s=1, marker='x', c='C0', label='The users located on the rows %i to %i (R%i to R%i)'%\n", - " (DeepMIMO_params['user_row_first'], DeepMIMO_params['user_row_last'],\n", - " DeepMIMO_params['user_row_first'], DeepMIMO_params['user_row_last']))\n", - "# First 181 users correspond to the first row\n", - "plt.scatter(DeepMIMO_dataset[active_bs_idx]['user']['location'][0:181, 1],\n", - " DeepMIMO_dataset[active_bs_idx]['user']['location'][0:181, 0],\n", - " s=1, marker='x', c='C1', label='First row of users (R%i)'% (DeepMIMO_params['user_row_first']))\n", - "\n", - "## Basestation location\n", - "plt.scatter(DeepMIMO_dataset[active_bs_idx]['location'][1],\n", - " DeepMIMO_dataset[active_bs_idx]['location'][0],\n", - " s=50.0, marker='o', c='C2', label='Basestation')\n", - "\n", - "plt.gca().invert_xaxis() # Invert the x-axis to align the figure with the figure above\n", - "plt.ylabel('x-axis')\n", - "plt.xlabel('y-axis')\n", - "plt.grid()\n", - "plt.legend();" + " # Plot the azimuth of the AoA\n", + "dataset_t.aoa_az.plot(cbar_title='Angle of Arrival (deg)')" ] }, { "cell_type": "markdown", "metadata": { - "id": "BnlfwY2futin" + "id": "YlwUGc-NtPmO" }, "source": [ "## Using DeepMIMO with Sionna\n", "\n", "The DeepMIMO Python package provides [a Sionna-compliant channel impulse response generator](https://nvlabs.github.io/sionna/phy/tutorials/CIR_Dataset.html#Generators) that adapts the structure of the DeepMIMO dataset to be consistent with Sionna.\n", "\n", - "An adapter is instantiated for a given DeepMIMO dataset. In addition to the dataset, the adapter takes the indices of the basestations and users, to generate the channels between these basestations and users:\n", - "\n", - "`DeepMIMOSionnaAdapter(DeepMIMO_dataset, bs_idx, ue_idx)`\n", - "\n", - "\n", - "**Note:** `bs_idx` and `ue_idx` set the links from which the channels are drawn. For instance, if `bs_idx = [0, 1]` and `ue_idx = [2, 3]`, the adapter then outputs the 4 channels formed by the combination of the first and second basestations with the third and fourth users.\n", - "\n", - "The default behavior for `bs_idx` and `ue_idx` are defined as follows:\n", - "- If value for `bs_idx` is not given, it will be set to `[0]` (i.e., the first basestation in the `DeepMIMO_dataset`).\n", - "- If value for `ue_idx` is not given, then channels are provided for the links between the `bs_idx` and all users (i.e., `ue_idx=range(len(DeepMIMO_dataset[0]['user']['channel']))`.\n", - "- If the both `bs_idx` and `ue_idx` are not given, the channels between the first basestation and all the users are provided by the adapter. For this example, `DeepMIMOSionnaAdapter(DeepMIMO_dataset)` returns the channels from the basestation 6 and the 9231 available user locations.\n", - "\n", - "**Note:** The adapter assumes basestations are transmitters and users are receivers. Uplink channels can be obtained using (transpose) reciprocity.\n", - "\n", - "### Random Sampling of Multi-User Channels\n", - "\n", - "When considering multiple basestations, `bs_idx` can be set to a 2D numpy matrix of shape $($ # of samples $\\times$ # of basestations per sample $)$. In this case, for each sample of basestations, the `DeepMIMOSionnaAdapter` returns a set of $($ # of basestations per sample $\\times$ # of users $)$ channels, which can be provided as a multi-transmitter sample for the Sionna model. For example, `bs_idx = np.array([[0, 1], [2, 3], [4, 5]])` provides three sets of $($ 2 basestations $\\times$ # of users $)$ channels. These three channel sets are from the basestation sets `[0, 1]`, `[2, 3]`, and `[4, 5]`, respectively, to the users.\n", + "An adapter is instantiated for a given DeepMIMO dataset. The adapter only requires the dataset to adapt and it will generate samples for all users and basestations in that dataset.\n", "\n", - "To use the adapter for multi-user channels, `ue_idx` can be set to a 2D numpy matrix of shape $($ # of samples $\\times$ # of users per sample $)$. In this case, for each sample of users, the `DeepMIMOSionnaAdapter` returns a set of $($ # of basestations $\\times$ # of users per sample $)$ channels, which can be provided as a multi-receiver sample for the Sionna model. For example, `ue_idx = np.array([[0, 1 ,2], [4, 5, 6]])` provides two sets of $($ # of basestations $\\times$ 3 users $)$ channels. These two channel sets are from the basestations to the user sets `[0, 1, 2]` and `[4, 5, 6]`, respectively.\n", + "In this example, `SionnaAdapter(dataset)` will return the channels from the basestation 6 and the 9231 available user locations.\n", "\n", - "In order to randomly sample channels from all the available user locations considering `num_rx` users, one may set `ue_idx` as in the following cell. In this example, the channels will be randomly chosen from the links between the basestation 6 and the 9231 available user locations." + "**Note:** The adapter assumes basestations are transmitters and users are receivers. Uplink channels can be obtained using (transpose) reciprocity." ] }, { @@ -330,32 +342,19 @@ "iopub.status.idle": "2025-03-13T01:48:52.380757Z", "shell.execute_reply": "2025-03-13T01:48:52.380192Z" }, - "id": "6d1ZcRNhutin" + "id": "6LDSxX9PtPmO" }, "outputs": [], "source": [ - "from DeepMIMO import DeepMIMOSionnaAdapter\n", - "\n", - "# Number of receivers for the Sionna model.\n", - "# MISO is considered here.\n", - "num_rx = 1\n", - "\n", - "# The number of UE locations in the generated DeepMIMO dataset\n", - "num_ue_locations = len(DeepMIMO_dataset[0]['user']['channel']) # 9231\n", - "# Pick the largest possible number of user locations that is a multiple of ``num_rx``\n", - "ue_idx = np.arange(num_rx*(num_ue_locations//num_rx))\n", - "# Optionally shuffle the dataset to not select only users that are near each others\n", - "np.random.shuffle(ue_idx)\n", - "# Reshape to fit the requested number of users\n", - "ue_idx = np.reshape(ue_idx, [-1, num_rx]) # In the shape of (floor(9231/num_rx) x num_rx)\n", - "\n", - "DeepMIMO_Sionna_adapter = DeepMIMOSionnaAdapter(DeepMIMO_dataset, ue_idx=ue_idx)" + "from deepmimo.integrations import SionnaAdapter\n", + "\n", + "deepmimo_sionna_adapter = SionnaAdapter(dataset_t)" ] }, { "cell_type": "markdown", "metadata": { - "id": "BhfzqX3qutin" + "id": "rl8wZ1O-tPmO" }, "source": [ "## Link-level Simulations using Sionna and DeepMIMO\n", @@ -375,7 +374,7 @@ "iopub.status.idle": "2025-03-13T01:48:52.397002Z", "shell.execute_reply": "2025-03-13T01:48:52.396376Z" }, - "id": "q_RJxXKEutin" + "id": "EeoY1rP9tPmO" }, "outputs": [], "source": [ @@ -484,7 +483,7 @@ { "cell_type": "markdown", "metadata": { - "id": "rMHYPBW4utin" + "id": "15OdNJoLtPmO" }, "source": [ "We next evaluate the setup with different $E_b/N_0$ values to obtain BLER curves." @@ -500,26 +499,29 @@ "iopub.status.idle": "2025-03-13T01:49:23.855650Z", "shell.execute_reply": "2025-03-13T01:49:23.854765Z" }, - "id": "q6tUA3qNutin", - "outputId": "698c42f5-9fc2-4ac2-8ea0-b57ac253d8d6" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WC1tSAq9tPmP", + "outputId": "26a07782-c588-4a32-c706-364f58b3dfe6" }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", "---------------------------------------------------------------------------------------------------------------------------------------\n", - " -7.0 | 1.2337e-01 | 1.0000e+00 | 28803 | 233472 | 128 | 128 | 7.4 |reached target block errors\n", - " -6.806 | 9.1728e-02 | 9.9219e-01 | 21416 | 233472 | 127 | 128 | 0.1 |reached target block errors\n", - " -6.611 | 6.0393e-02 | 9.3750e-01 | 14100 | 233472 | 120 | 128 | 0.1 |reached target block errors\n", - " -6.417 | 2.7489e-02 | 7.6562e-01 | 9627 | 350208 | 147 | 192 | 0.1 |reached target block errors\n", - " -6.222 | 6.6111e-03 | 3.9844e-01 | 3087 | 466944 | 102 | 256 | 0.2 |reached target block errors\n", - " -6.028 | 9.2416e-04 | 9.9265e-02 | 1834 | 1984512 | 108 | 1088 | 0.9 |reached target block errors\n", - " -5.833 | 7.8896e-05 | 1.3594e-02 | 921 | 11673600 | 87 | 6400 | 5.1 |reached max iterations\n", - " -5.639 | 9.8513e-06 | 2.0313e-03 | 115 | 11673600 | 13 | 6400 | 5.2 |reached max iterations\n", - " -5.444 | 4.2832e-07 | 1.5625e-04 | 5 | 11673600 | 1 | 6400 | 5.2 |reached max iterations\n", - " -5.25 | 0.0000e+00 | 0.0000e+00 | 0 | 11673600 | 0 | 6400 | 5.1 |reached max iterations\n", + " -7.0 | 1.2781e-01 | 1.0000e+00 | 29840 | 233472 | 128 | 128 | 7.4 |reached target block errors\n", + " -6.806 | 9.7352e-02 | 1.0000e+00 | 22729 | 233472 | 128 | 128 | 1.0 |reached target block errors\n", + " -6.611 | 6.6376e-02 | 9.4531e-01 | 15497 | 233472 | 121 | 128 | 1.0 |reached target block errors\n", + " -6.417 | 3.0492e-02 | 7.9688e-01 | 7119 | 233472 | 102 | 128 | 1.0 |reached target block errors\n", + " -6.222 | 1.1498e-02 | 4.7656e-01 | 5369 | 466944 | 122 | 256 | 2.1 |reached target block errors\n", + " -6.028 | 1.4813e-03 | 1.3281e-01 | 2075 | 1400832 | 102 | 768 | 6.8 |reached target block errors\n", + " -5.833 | 1.6177e-04 | 2.2645e-02 | 1303 | 8054784 | 100 | 4416 | 38.1 |reached target block errors\n", + " -5.639 | 1.4563e-05 | 3.9062e-03 | 170 | 11673600 | 25 | 6400 | 56.0 |reached max iterations\n", + " -5.444 | 3.5979e-06 | 6.2500e-04 | 42 | 11673600 | 4 | 6400 | 54.6 |reached max iterations\n", + " -5.25 | 0.0000e+00 | 0.0000e+00 | 0 | 11673600 | 0 | 6400 | 57.5 |reached max iterations\n", "\n", "Simulation stopped as no error occurred @ EbNo = -5.2 dB.\n", "\n" @@ -527,16 +529,16 @@ } ], "source": [ - "sim_params = {\n", - " \"ebno_db\": np.linspace(-7, -5.25, 10),\n", + "sim_params = {\"ebno_db\": np.linspace(-7, -5.25, 10),\n", " \"cyclic_prefix_length\" : 0,\n", - " \"pilot_ofdm_symbol_indices\" : [2, 11],\n", - " }\n", + " \"pilot_ofdm_symbol_indices\" : [2, 11]}\n", + "\n", "batch_size = 64\n", - "model = LinkModel(DeepMIMO_Sionna_adapter=DeepMIMO_Sionna_adapter,\n", - " carrier_frequency=DeepMIMO_params['scenario_params']['carrier_freq'],\n", + "model = LinkModel(DeepMIMO_Sionna_adapter=deepmimo_sionna_adapter,\n", + " carrier_frequency=dataset_t.rt_params.frequency,\n", " cyclic_prefix_length=sim_params[\"cyclic_prefix_length\"],\n", " pilot_ofdm_symbol_indices=sim_params[\"pilot_ofdm_symbol_indices\"])\n", + "\n", "ber, bler = sim_ber(model,\n", " sim_params[\"ebno_db\"],\n", " batch_size=batch_size,\n", @@ -555,29 +557,33 @@ "iopub.status.idle": "2025-03-13T01:49:24.255402Z", "shell.execute_reply": "2025-03-13T01:49:24.254518Z" }, - "id": "f91WTHfAutin", - "outputId": "4a7a1bd8-0c3b-489a-bb3f-64de186a12f7" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 716 + }, + "id": "q5G8_EU6tPmP", + "outputId": "8c3bffcc-d47d-4cde-9c65-0f6c984ea3cb" }, "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 8, "metadata": {}, - "output_type": "execute_result" + "execution_count": 11 }, { + "output_type": "display_data", "data": { - "image/png": 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", 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\n" }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} } ], "source": [ @@ -591,7 +597,7 @@ { "cell_type": "markdown", "metadata": { - "id": "qfue1Q_7utio" + "id": "MY82CFF9tPmP" }, "source": [ "## DeepMIMO License and Citation\n",