This is the code for paper Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Rate Control. The model is implemented with PyTorch.
The basic settings are contained in options/base_options.py, options/train_options.py, and options/test_options.py. The style of coding is borrowed from CycleGAN.
usage: train_dyna.py [-h] [--gpu_ids GPU_IDS]
[--checkpoints_dir CHECKPOINTS_DIR] [--model MODEL]
[--input_nc INPUT_NC] [--output_nc OUTPUT_NC] [--ngf NGF]
[--ndf NDF] [--max_ngf MAX_NGF] [--norm NORM]
[--init_type INIT_TYPE] [--init_gain INIT_GAIN]
[--n_downsample N_DOWNSAMPLE] [--n_blocks N_BLOCKS]
[--C_channel C_CHANNEL] [--G_n G_N] [--G_s G_S]
[--select SELECT] [--SNR_MAX SNR_MAX] [--SNR_MIN SNR_MIN]
[--lambda_reward LAMBDA_REWARD] [--lambda_L2 LAMBDA_L2]
[--batch_size BATCH_SIZE]
[--max_dataset_size MAX_DATASET_SIZE] [--epoch EPOCH]
[--load_iter LOAD_ITER] [--verbose] [--suffix SUFFIX]
[--save_latest_freq SAVE_LATEST_FREQ]
[--print_freq PRINT_FREQ]
[--save_epoch_freq SAVE_EPOCH_FREQ] [--save_by_iter]
[--continue_train] [--epoch_count EPOCH_COUNT]
[--phase PHASE] [--n_epochs_joint N_EPOCHS_JOINT]
[--n_epochs_decay N_EPOCHS_DECAY]
[--n_epochs_fine N_EPOCHS_FINE] [--lr_joint LR_JOINT]
[--lr_decay LR_DECAY] [--lr_fine LR_FINE]
[--temp_init TEMP_INIT] [--eta ETA]
Example usage:
python train_dyna.py --gpu_ids '0' --select 'hard' --SNR_MIN 0 --SNR_MAX 20 --lambda_reward 2e-3
usage: test_dyna.py [-h] [--gpu_ids GPU_IDS]
[--checkpoints_dir CHECKPOINTS_DIR] [--model MODEL]
[--input_nc INPUT_NC] [--output_nc OUTPUT_NC] [--ngf NGF]
[--ndf NDF] [--max_ngf MAX_NGF] [--norm NORM]
[--init_type INIT_TYPE] [--init_gain INIT_GAIN]
[--n_downsample N_DOWNSAMPLE] [--n_blocks N_BLOCKS]
[--C_channel C_CHANNEL] [--G_n G_N] [--G_s G_S]
[--select SELECT] [--SNR_MAX SNR_MAX] [--SNR_MIN SNR_MIN]
[--lambda_reward LAMBDA_REWARD] [--lambda_L2 LAMBDA_L2]
[--batch_size BATCH_SIZE]
[--max_dataset_size MAX_DATASET_SIZE] [--epoch EPOCH]
[--load_iter LOAD_ITER] [--verbose] [--suffix SUFFIX]
[--phase PHASE] [--num_test NUM_TEST]
[--num_test_channel NUM_TEST_CHANNEL] [--SNR SNR]
Example usage:
python test_dyna.py --gpu_ids '0' --select 'hard' --SNR_MIN 0 --SNR_MAX 20 --lambda_reward 2e-3 --num_test 10000 --num_test_channel 1 --SNR 5
@misc{yang2021deep,
title={Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Rate Control},
author={Mingyu Yang and Hun-Seok Kim},
year={2021},
eprint={2110.04456},
archivePrefix={arXiv},
primaryClass={eess.SP}
}