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.

翻译：LoRa因其低成本、长距离和极低的传输功耗，已成为低功耗广域网中广泛采用的无线调制方案。然而，在密集部署的LoRa网络中，相同扩频因子帧之间频繁发生的碰撞，会阻碍传统LoRa接收机对帧的检测与正确解码。尽管近期研究提出了改进恢复的方法，但其检测阶段在低信噪比和高碰撞率条件下性能急剧下降。本文提出LZn——一种基于频谱交叉运算的低复杂度同步方案。我们的方法即使在多包重叠或极低信噪比条件下，仍能实现鲁棒的帧同步。通过仿真及三个独立真实LoRa数据集评估，LZn将检测灵敏度提升高达10dB，检测概率提升达1.54倍。在真实数据集中，与次优的抗碰撞方案TnB相比，LZn在最具挑战性的单用户场景下解码性能提升3.46倍，碰撞场景下提升达1.22倍。这些结果表明，LZn在保持实时性兼容的前提下，显著提升了LoRa接收机的帧恢复能力。