LZn : Robust LoRa Frame Synchronization Under Frame Collisions and Ultra-Low SNR Conditions
For LoRa network operators, LZn enables reliable frame recovery in dense deployments with high collision rates and low SNR, addressing a critical bottleneck in LPWAN scalability.
LZn introduces a low-complexity synchronization scheme using spectral intersection to robustly synchronize LoRa frames under collisions and ultra-low SNR, improving detection sensitivity by up to 10 dB and detection probability by up to 1.54x, with decoding improvements of 3.46x in single-user and 1.22x in collision scenarios over the second best scheme.
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.