CoRa: A Collision-Resistant LoRa Symbol Detector of Low Complexity

TL;DR

CoRa is a low-complexity, collision-resistant LoRa symbol detector that employs a Bayesian classifier to improve decoding accuracy under severe interference, outperforming existing methods significantly.

Contribution

Introduces CoRa, a novel Bayesian-based symbol detector for LoRa that reduces complexity and enhances collision resilience without relying on peak detection.

Findings

Up to 29% better decoding than TnB

178% improvement over CIC

Packet reception rate increased by up to 11.53x

Abstract

Long range communication with LoRa has become popular as it avoids the complexity of multi-hop communication at low cost and low energy consumption. LoRa is openly accessible, but its packets are particularly vulnerable to collisions due to long time on air in a shared band. This degrades communication performance. Existing techniques for demodulating LoRa symbols under collisions face challenges such as high computational complexity, reliance on accurate symbol boundary information, or error-prone peak detection methods. In this paper, we introduce CoRa , a symbol detector for demodulating LoRa symbols under severe collisions. CoRa employs a Bayesian classifier to accurately identify the true symbol amidst interference from other LoRa transmissions, leveraging empirically derived features from raw symbol data. Evaluations using real-world and simulated packet traces demonstrate that CoRa clearly outperforms the related state-of-the-art, i.e., up to 29% better decoding performance than TnB and 178% better than CIC. Compared to the LoRa baseline demodulator, CoRa magnifies the packet reception rate by up to 11.53x. CoRa offers a significant reduction in computational complexity compared to existing solutions by only adding a constant overhead to the baseline demodulator, while also eliminating the need for peak detection and accurately identifying colliding frames.