Indoor Pathloss Radio Map Prediction

Implementation for indoor radio map prediction using Vision Transformers.

Paper: Vision Transformers for Efficient Indoor Pathloss Radio Map Prediction

Environment Setup

Create and activate the Conda environment:

conda env create
conda activate indoor_pathloss

Configuration

1. Copy the example environment file:

   cp .env.example .env

2. Edit .env and set the required paths:

   • PREDICTIONS_PATH — directory for inference outputs
   • OUTPUT_DIR — directory for training outputs and run logs
   • AIM_REPO — path for Aim experiment logging
   • ICASSP_ORIG_PATH — path to original ICASSP dataset
   • ICASSP_TASK1_PATH, ICASSP_TASK2_PATH, ICASSP_TASK3_PATH — paths to preprocessed task data

3. Customize configs/ as needed (e.g., checkpoint path in configs/inference.yaml, prediction path in configs/evaluation.yaml).

Running the Code

Scripts in the run/ directory should be executed from the project root.

Script	Purpose
run/train_task1.sh	Train model for ICASSP Task 1
run/train_task2.sh	Train model for ICASSP Task 2
run/train_task3.sh	Train model for ICASSP Task 3
run/inference.sh	Run inference (uses checkpoint and data paths from configs)
run/evaluation.sh	Evaluate predictions (MSE)

Project Structure

• src/ — algorithms, datamodules, networks
• configs/ — Hydra configuration files
• run/ — executable scripts for training, inference, and evaluation
• jupyter/ — notebooks for inference