AlignTrack: Push the SNR Limit of LoRa Collision Decoding

Abstract

LoRa has been shown as a promising Low-Power Wide Area Network (LPWAN) technology to connect millions of devices for the Internet of Things by providing long-distance low-power communication when the SNR is very low. Real LoRa networks, however, suffer from severe packet collisions. Existing collision resolution approaches introduce a high SNR loss, i.e., require a much higher SNR than LoRa. To push the limit of LoRa collision decoding, we present AlignTrack, the first LoRa collision decoding approach that can work in the SNR limit of the original LoRa. Our key finding is that a LoRa chirp aligned with a decoding window should lead to the highest peak in the frequency domain and thus has the least SNR loss. By aligning a moving window with different packets, we separate packets by identifying the aligned chirp in each window. We theoretically prove this leads to the minimal SNR loss. In practical implementation, we address two key challenges: (1) accurately detecting the start of each packet, and (2) separating collided packets in each window in the presence of CFO and inter-packet interference. We implement AlignTrack on HackRF One and compare its performance with the state-of-the-arts. The evaluation results show that AlignTrack improves network throughput by 1.68× compared with NScale and 3× compared with CoLoRa.

Deep Learning Collision Aware Transmission Scheduling for Dense LPWANs

“…Different approaches are proposed to address the collision problem for LoRa. To push the limit of LoRa collision decoding under low SNR, the authors in [23] propose Align-Track, a novel LoRa packet collision decoding approach to minimize SNR loss, which is different from the existing approaches that introduce non-negligible SNR loss and require a higher SNR than traditional LoRa. AlignTrack leverages the entire chirp to concentrating the whole energy and can decode collisions in the SNR limit of the original LoRa (SNR≈−20 dB).…”