LZn : Robust LoRa Frame Synchronization Under Frame Collisions and Ultra-Low SNR Conditions

TL;DR

LZn is a novel low-complexity synchronization scheme that significantly enhances LoRa frame detection and decoding under high collision and low SNR conditions, outperforming existing methods.

Contribution

The paper introduces LZn, a spectral intersection-based synchronization method that improves LoRa frame detection robustness in challenging environments.

Findings

LZn increases detection sensitivity by up to 10dB.

LZn improves detection probability by up to 1.54x.

LZn enhances decoding performance by 3.46x in single-user scenarios.

Abstract

LoRa has become a widely adopted wireless modulation scheme in LPWANs due to its low cost, long range, and minimal transmission power. However, collisions between frames of the same spreading factor -- common in dense LoRa deployments -- prevent conventional LoRa receivers from detecting and correctly decoding frames. Recent work has introduced methods to improve recovery, yet their detection stage degrades sharply under low signal-to-noise ratio (SNR) and high collision rates. In this work, we introduce LZn, a low-complexity synchronization scheme driven by a spectral intersection operation. Our method enables robust frame synchronization even under multiple packet overlaps or extremely low SNR conditions. We evaluate LZn on simulations and three independent, real-world LoRa datasets. LZn improves detection sensitivity by up to 10dB and increases detection probability by up to 1.54x. In real-world datasets, LZn improves decoding by 3.46x in the most challenging single-user scenario and up to 1.22x in collision scenarios compared to the second best collision-tolerant scheme (TnB). These results demonstrate that LZn substantially improves the frame recovery of LoRa receivers, while remaining compatible with real-time requirements.