mingyuyng / Deep-JSCC-for-images-with-OFDM

Codes for "Deep Joint Source Channel Coding for Wireless Image Transmission with OFDM", accepted by ICC 2021

Deep-JSCC-for-images-with-OFDM

Environments

python=3.8.0

numpy=1.24.4

pytorch=1.13.1+cu117

cuda=12.2

dominate=2.8.0

scipy=1.10.1

visdom=0.2.4

Test the OFDM model

The test script for the OFDM system implementation is in models/channel.py.

Datasets

This repository contains codes for CIFAR-10 and CelebA. For CelebA, you will need to download the dataset under data folder. You can also use other datasets but you need to customize the dataloader. One example is data/CelebA_dataset.py.

Train the model

All available options are under options folder. Change --feedforward for different models. For example, set feedforward as 'IMPLICIT' for IMPLICIT model in the paper. Set feedforward as 'EXPLICIT-RES' for EXPLICIT model in the paper.

One example for training:

python train.py --gpu_ids '0' --feedforward 'EXPLICIT-RES' --N_pilot 2 --n_downsample 2 --C_channel 12 --S 6 
  --SNR 20 --dataset_mode 'CIFAR10' --n_epochs 200 --n_epochs_decay 200 --lr 1e-3 

Suppose the input image has a size of C x W x H. To keep the size consistent, you would need to satisfy the requirement: WH/(2^(2xn_downsample))xC_channel = Sx128

Reference

Mingyu Yang, Chenghong Bian, Hun-Seok Kim, "Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM", accepted by ICC 2021

@article{yang2021deep,
    title={Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM},
    author={Yang, Mingyu and Bian, Chenghong and Kim, Hun-Seok},
    journal={arXiv preprint arXiv:2101.03909},
    year={2021}
}