MVSC: Mamba Vision based Semantic Communication for Image Transmission with SNR Estimation

Pytorch implementation for MVSC: Mamba Vision based Semantic Communication for Image Transmission with SNR Estimation | IEEE Journals & Magazine | IEEE Xplore

Introduction

This paper proposes a novel semantic communication approach named Mamba Vision-based Semantic Communication (MVSC) for image transmission with integrated Signal-to-Noise Ratio (SNR) estimation. Unlike prior works that assume the SNR of the received signal is known and input a predetermined SNR value into a deep learning (DL) network, MVSC introduces an implicit SNR estimation module, allowing the network to infer channel conditions for SNR adaptation. To further improve performance, we propose the MVSC4, a joint-optimized of MVSC, which is trained using a multi-task learning strategy that simultaneously optimizes image reconstruction, SNR estimation, signal denoising, and image classification. This joint optimization enhances the network’s robustness to varying SNR conditions, particularly in low-SNR environments. Comparative experiments on CIFAR-10 and Kodak datasets demonstrate that MVSC4 outperforms both CNN-based and Transformer-based methods in terms of Peak Signal-to-Noise Ratio (PSNR) and Multiscale Structural Similarity (MS-SSIM). The results demonstrate the effectiveness and robustness of the proposed approach.

Python environment

einops==0.8.1
mamba_ssm==1.1.0
matplotlib==3.10.3
numpy==2.2.5
Pillow==11.2.1
scipy==1.15.3
sionna==0.19.0
tensorboardX==2.6.2.2
tensorboardX==2.6.2.2
tensorflow==2.14.0
thop==0.1.1.post2209072238
timm==0.4.12
torch==2.1.1+cu118
torchvision==0.16.1+cu118
tqdm==4.66.4

Usage

## setting config.py and run train.py
python train.py         

## run test.py to get result. This step is generally not needed, as the test results are already generated during training.
python test.py

Citation

If this work is useful for your research, please cite:

@article{li2025mvsc,
  title={MVSC: Mamba Vision based Semantic Communication for Image Transmission with SNR Estimation},
  author={Li, Chongyang and Zhang, Tianqian and Liu, Shouyin},
  journal={IEEE Communications Letters},
  year={2025},
  publisher={IEEE}
}

Related links

• SwinJSCC:https://github.com/semcomm/SwinJSCC
• MambaVision:https://github.com/NVlabs/MambaVision
• Sionna for Next Generation Physical Layer research:https://github.com/NVlabs/sionna
• BPG image encoder and decoder: https://bellard.org/bpg
• CIFAR100: https://www.cs.toronto.edu/~kriz/cifar.html

Thank you for your outstanding contributions!