CurvingLoRa

This repository contains scripts and instructions for reproducing the experiments in our NSDI'22 paper " CurvingLoRa to Boost LoRa Network Capacity via Concurrent Transmission".

Overview

• Environment Setting
• Repo Structure
• Reproduce Results with Provided Logs
• Reproduce Results from Scratch
• Notes
• Contact

Environment Setting

Required Software:

• MatLab - Processing & Algorithm
• GNU Radio - Data Collection

Run the following commands to download CurvingLoRa from GitHub.

git clone https://github.com/liecn/CurvingLoRa_NSDI22.git
cd CurvingLoRa_NSDI22
cp config_example.m config.m
# update the HOME_DIR in the config.m

Repo Structure

Repo Root
+-- 0_demo                  # A toy example for non-linear chirp generation.
+-- 1_observation           # Fig 5(a)-(d), Fig 7(a)-(d).
+-- 2_simulation            # Fig 6(a)-(b), Fig 8(a)-(b).
+-- 3_deployment            # Evalution Part
    +-- symbol_emulation    # Fig 16(a)-(b), Fig 17(a)-(d), Fig 18(a)-(d).
    +-- outdoor_emulation   # Fig 20(a)
    +-- result              # Results
    +-- transmitter         # Matlab scripts for packet generation
+-- data                    # Dataset
    +-- symbol              # Indoor symbol dataset
    +-- outdoor             # Outdoor dataset
    +-- groundtruth         # Groundtruth for the outdoor dataset
+-- utils                   # Utility functions
+-- figs                    # Some figures in the paper
+-- config_example.m        # Configuration template

Reproduce Results with Provided Logs

We provide the performance results under the coresponding directory result/. To reproduce the figures in the paper, you can run following commands for python scripts.

cd CurvingLoRa_NSDI22
matlab -nodisplay

%% Matlab
addpath(genpath('./.'));

%% Observation Results
1_observation/fig_5       #Fig 5
1_observation/fig_7       #Fig 7

%% Simulation Results
2_simulation/fig_6a      #Fig 5
2_simulation/fig_6b      #Fig 7
2_simulation/fig_8a      #Fig 8a  
2_simulation/fig_8b      #Fig 8b
2_simulation/fig_sir2map #SIR map

%% Evaluation Results
3_deployment/symbol_emulation/fig_16a      #Fig 16a
3_deployment/symbol_emulation/fig_16b      #Fig 16b
3_deployment/symbol_emulation/fig_17abcd   #Fig 17abcd
3_deployment/symbol_emulation/fig_18abcd   #Fig 18abcd
3_deployment/symbol_emulation/fig_17abcd   #Fig 20a
3_deployment/outdoor_emulation/figs_outdoor_emulation   #Fig 20a

Reproduce Results from Scratch

Please assure that all paths in the configurations are consistent for datasets, scripts, and logs.

1. Download datasets. Please download symbol and outdoor datasets and put them under the data/, as shown in the directory tree above.

2. Please run the eva_{***} scripts under each directory to reproduce the results from Scratch.

Notes

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

@inproceedings{CurvingLoRa_NSDI22,
    author = {Li, Chenning and Guo, Xiuzhen and Shuangguan, Longfei and Cao, Zhichao and Jamieson, Kyle},
    title = {CurvingLoRa to Boost LoRa Network Throughput via Concurrent Transmission},
    year = {2022},
    booktitle = {Proceedings of USENIX NSDI},
}

Contact

Chenning Li by lichenni@msu.edu