Wireless Channels as Fingerprints: Towards Collision-Free LoRa Networks

LoRa enables long-range Internet of Things (IoT) connectivity but suffers from collision issues in dense deployments, where concurrent transmissions overlap at gateways, leading to decoding errors. Existing solutions rely on time/frequency separation or protocol modifications, requiring either hardware changes or dedicated codings, and failing to resolve collisions that are completely aligned in time or frequency. We present CD-LoRa, a Channel-Division based LoRa parallel transmission scheme for LoRa collisions. CD-LoRa exploits distinct channel signatures as inherent orthogonal fingerprints, enabling parallel decoding even when collisions are completely aligned. We present a phase calibration model that decouples genuine channel features from hardware imperfections and payload modulation distortions. We enhance low-SNR LoRa signals through energy-concentration processing. We address channel variations in mobile scenarios with a dynamic temporal sequence based clustering design. We implement CD-LoRa on commodity LoRa devices and evaluate its performance in real-world deployments. Experimental results show that CD-LoRa effectively decodes up to 8 time-frequency-aligned packets, and improves network throughput by 1.72× compared to state-of-the-art methods.