FlipLoRa: Resolving Collisions with Up-Down Quasi-Orthogonality
Zhenqiang Xu, Shuai Tong, Pengjin Xie, Jiliang Wang
Abstract
LoRa is recently a rising star in Low Power Wide Area Network (LPWAN) family to provide low power and long range communication for large number of devices in Internet of Things. LoRa is based on Chirp Spread Spectrum (CSS) and uses chirp frequency shift to encode data. It has been shown that collision significantly degrades LoRa performance in practice. We propose FlipLoRa, a new mechanism to disentangle LoRa collisions, which allows concurrent transmission of multiple packets. The key idea of FlipLoRa is to utilize the quasi-orthogonality between upchirp and downchirp. FlipLoRa encodes packets with interleaved upchirps and downchirps instead of only using upchirps as in LoRa. We then propose a novel method to disentangle chirps and decode multiple collided packets. To evaluate the performance, we formally prove the quasi-orthogonality and analyze its applicable conditions. We validate the performance improvement by theoretical analysis. Further, we implement FlipLoRa on software-defined radio and extensively evaluate its performance for real LoRa networks. The evaluation results show that FlipLoRa can improve the throughput by 3.84x over LoRa physical layer.