NELoRa-Bench

This folder provides the neural-based LoRa demodulation code for our ICLR 2023 workshop paper: NELoRa-Bench: A Benchmark for Neural-enhanced LoRa Demodulation The dataset and checkpoint can be accessed at this Google Drive link

This code reproduces the experiments in the SenSys '21 paper "NELoRa: Towards Ultra-low SNR LoRa Communication with Neural-enhanced Demodulation".

Differences from the original code provided by NELoRa:

Now for both nelora and baseline train/test, no need for a separate stage of data-generation (adding artificial noise). Noise is added on-the-fly. This reduces overfitting issues and removes the need for additional harddisk space, also speeding up the process drastically.
Added data balancing.
Removed clutter.
Parameters are partially hardcoded.
Add a double check on the dataset for wrong codes.
Add comparison with baseline methods, using LoRaPhy from From Demodulation to Decoding: Toward Complete LoRa PHY Understanding and Implementation

Usage:

Download dataset and unzip them from Google Drive.
Download checkpoints from the same Google Drive. Note: these checkpoints are only trained for a limited amount of time and can be improved.
Adjust the parameters in main.py:

# parameters
parser = argparse.ArgumentParser() 
parser.add_argument('--sf', type=int, help='The spreading factor.') 
parser.add_argument('--batch_size', type=int, default=16, help='The batch size.') 
opts = parser.parse_args()
sf = opts.sf  # spreading factor
batch_size = opts.batch_size  # batch size (the larger, the better, depending on GPU memory)

bw = 125e3  # bandwidth
fs = 1e6  # sampling frequency
data_dir = f'/path/to/NeLoRa_Dataset/{sf}/'  # directory for training dataset
mask_CNN_load_path = f'checkpoint/sf{sf}/100000_maskCNN.pkl'  # path for loading mask_CNN model weights
C_XtoY_load_path = f'checkpoint/sf{sf}/100000_C_XtoY.pkl'  # path for loading mask_CNN model weights
save_ckpt_dir = 'ckpt'  # directory for saving trained weight checkpoints
normalization = True  # whether to perform normalization on data
snr_range = list(range(-30, 1))  # range of SNR for training
test_snr = -17  # SNR for testing
scaling_for_imaging_loss = 128  # scaling of losses between mask_CNN and C_XtoY
ckpt_per_iter = 1000  # checkpoint per iteration
train_epochs = 100  # how many epochs to train (the larger, the better, network will not overfit)

For training, call train(). For testing, call test().

if __name__ == '__main__':
    train()
    # test()

Run main.py with appropriate prameters, e.g.:

python main.py --sf 7 --batch_size 128

please consider to cite our paper if you use the code or data in your research project.

  @inproceedings{nelora2021sensys,
  	title={{NELoRa: Towards Ultra-low SNR LoRa Communication with Neural-enhanced Demodulation}},
  	author={Li, Chenning and Guo, Hanqing and Tong, Shuai and Zeng, Xiao and Cao, Zhichao and Zhang, Mi and Yan, Qiben and Xiao, Li and Wang, Jiliang and Liu, Yunhao},
    	booktitle={In Proceeding of ACM SenSys},
    	year={2021}
  }

daibiaoxuwu/NeLoRa_Dataset

NELoRa-Bench