SLoRa : A Systematic Framework for Synergic Interference Resilience in LPWAN

By offering long-range wireless connections with low energy consumption, Low-Power Wide Area Networks (LP-WANs) have been widely deployed in many critical Internet-of-Things (IoT) applications. However, LPWAN's low-power nature leads to inevitable packet corruption under cross-Technology interference (CTI) emitted from other wireless technologies that coexist in the same unlicensed band. To address this fundamental issue, researchers propose multiple lines of solutions, such as signal analysis and coding. Despite these efforts, the research on CTI mitigation needs to go one step further-to design a systematic framework for enhanced interference resilience.
This paper introduces SLoRa, a systematic framework inte-grating symbol recovery with soft decoding for synergic inter-ference resilience. Without loss of generality, we focus on LoRa, the dominating LPWAN technology. SLoRa's symbol recovery examines the corrupted signal through a two-stage analysis to recover incorrect LoRa symbols accurately at a low cost. To estimate the confidence of recovered symbols, SLoRa examines the statuses of LoRa and CTI, and it further leverages the estimated confidence via soft decoding to improve the error correction capability. We analyze SLoRa theoretically and evaluate it with real testbeds, including SX1280 LoRa chips and USRP B210. Experiments across multiple settings, e.g., different CTI sources and frequency bands, demonstrate that SLoRa effectively enhances interference resilience at a low cost. Compared with the state-of-The-Art technique, SLoRa improves the CTI protection capability by 1.4x while only requiring 1 % computation cost. © 2023 IEEE.