CurvingLoRa to Boost LoRa Network Throughput via Concurrent Transmission


Chenning Li, Michigan State University; Xiuzhen Guo, Tsinghua University; Longfei Shangguan, University of Pittsburgh & Microsoft; Zhichao Cao, Michigan State University; Kyle Jamieson, Princeton University.


LoRaWAN has emerged as an appealing technology to connect IoT devices but it functions without explicit coordination among transmitters, which can lead to many packet collisions as the network scales. State-of-the-art work proposes various approaches to deal with these collisions, but most functions only in high signal-to-interference ratio (SIR) conditions and thus does not scale to real scenarios where weak receptions are easily buried by stronger receptions from nearby transmitters. In this paper, we take a fresh look at LoRa’s physical layer, revealing that its underlying linear chirp modulation fundamentally limits the capacity and scalability of concurrentLoRa transmissions. We show that by replacing linear chirps with their non-linear counterparts, we can boost the throughput of concurrent LoRa transmissions and empower the LoRa receiver to successfully receive weak transmissions in the presence of strong colliding signals. Such a non-linear chirp design further enables the receiver to demodulate fully aligned collision symbols — a case where none of the existing approaches can deal with. We implement these ideas in a holistic LoRaWAN stack based on the USRP N210 software-defined radio platform. Our head-to-head comparison with two state-of-the-art research systems and a standard LoRaWAN base-line demonstrates that CurvingLoRa improves the network throughput by 1.6–7.6× while simultaneously sacrificing neither power efficiency nor noise resilience

NSDI '22 Open Access Sponsored by
King Abdullah University of Science and Technology (KAUST)

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

@inproceedings {278330,
author = {Chenning Li and Xiuzhen Guo and Longfei Shangguan and Zhichao Cao and Kyle Jamieson},
title = {{CurvingLoRa} to Boost {LoRa} Network Throughput via Concurrent Transmission},
booktitle = {19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22)},
year = {2022},
isbn = {978-1-939133-27-4},
address = {Renton, WA},
pages = {879--895},
url = {},
publisher = {USENIX Association},
month = apr

Presentation Video