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.

LoRa, as a representative of Low Power Wide Area Network (LPWAN) technology, has attracted significant attention from both academia and industry. However, the current understanding of LoRa is far from complete, and implementations have a large performance gap in SNR and packet reception rate. This paper presents a comprehensive understanding of LoRa PHY (physical layer protocol) and reveals the fundamental reasons for the performance gap. We present the first full-stack LoRa PHY implementation with a provable performance guarantee. We enhance the demodulation to work under extremely low SNR (− 20 dB) and analytically validate the performance, where many existing works require SNR > 0. We derive the order and parameters of decoding operations, including dewhitening, error correction, deinterleaving, etc., by leveraging LoRa features and packet manipulation. We implement a complete real-time LoRa on the GNU Radio platform and conduct extensive experiments. Our method can achieve (1) a 100% decoding success rate while existing methods can support at most 66.7%, (2) -142 dBm sensitivity, which is the limiting sensitivity of the commodity LoRa, and (3) a 3600 m communication range in the urban area, even better than commodity LoRa under the same setting.

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