Usability improvement on the original repo hanqingguo/NELoRa-Sensys 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. Changes are kept minimum for consistency. Other usability issues, like data balancing and testing during training, are not addressed.
For decoding, read neural_enhanced_demodulation/README.md. neural-based decoding is in neural_enhanced_demodulation/pytorch, baseline method is in neural_enhanced_demodulation/matlab.
Readme from the original repo:
This repository provides the codes for our SenSys 2021 paper
This repository contains scripts and instructions for reproducing the experiments in our SenSys '21 paper "NELoRa: Towards Ultra-low SNR LoRa Communication with Neural-enhanced Demodulation".
If you have any questions or comments, please post in the Issues on Github.
NELoRa won the Best Paper Award at SenSys '21!
This repo is actively maintained currently.
Illustrated in the following figure, our repo is composed of two modules, including the Symbol Generation and the neural-enhanced demodulation.
Please find the dataset, instruction and source code of each module in the corresponding directory.
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}
}