From Demodulation to Decoding: Toward Complete LoRa PHY Understanding and Implementation

Abstract

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.

AlignTrack: Push the SNR Limit of LoRa Collision Decoding

“…FlipLoRa [50] encodes packets with interleaved upchirps and downchirps instead of only using upchirps as in LoRa. This work [51] presents a comprehensive understanding of LoRa PHY (physical layer protocol) and reveals the fundamental reasons for the performance gap. Ameloot et al [21] presents an independently developed packet reception algorithm, which drastically improves the physical performance of LoRa communication links.…”

Optimizing spectral utilization of LPWANs

“…One of the earliest comprehensive reverse engineering eorts of LoRa PHY was brought forward by Knight [20], which was later enhanced by Peiter et al [55]. These seminal studies paved the way for further reverse engineering eorts in the eld, including a notable study [56], where the authors improved the SNR performance of the demodulation pipeline to detect low SNR frames.…”

“…Detailed insights on LoRa demodulation are provided in Chapter 5, but here we provide a quick summary to set the stage for discussing collision resolution methods. A LoRa receiver leverages the preamble and the Start Frame Delimiter (SFD) of a LoRa frame to accurately anticipate the frame's start time [56]. This facilitates appropriate separation of the I/Q samples of each chirp into individual windows.…”