SlideLoRa: Reliable Channel Activity Monitoring across Massive Logical Channels in LoRa Networks

• Published in: Proceedings - International Conference on Network Protocols pp. 1 - 12
• Main Authors: Jiang, Jiamin, Yu, Shiming, Wang, Hao, Li, Haiyang, Zheng, Yuanqing, Wang, Lu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 22.09.2025
• Subjects: Channel Activity Detection; Cross-Channel; Demodulation; Frequency-domain analysis; Internet of Things; LoRa; Low-Power Wide-Area Networks; Monitoring; Physical layer; Protocols; Robustness; Scalability; Signal to noise ratio; Symbols
• ISSN: 2643-3303
• DOI: 10.1109/ICNP65844.2025.11192378

LoRa technology has been extensively implemented in various IoT applications, offering widespread low-power connectivity for millions of nodes across thousands of logical channels. However, current LoRa networks lack an efficient mechanism for monitoring channel activity across these numerous channels, which prevents network operators from effectively detecting physical layer activities and implementing additional functionalities (e.g., channel access control). Existing solutions either involve complex iterations over each logical channel or fail to detect extremely weak packets in low SNR conditions. These limitations affect their scalability and robustness in monitoring the vast number of logical channels available in the LoRa spectrum. To address this issue, this paper introduces SlideLoRa, an innovative packet detection method that enables detection across all logical channels under various channel conditions. SlideLoRa consolidates the complete energy of LoRa symbols using an expanded demodulation window combined with a fine-grained sliding window, effectively reconstructing the distorted frequency-domain information of LoRa packets. To achieve this, SlideLoRa incorporates a series of novel solutions, including peak tracking in low SNR, peak sequence matching, peak extraction, and packet parameter retrieval. Experimental results demonstrate that SlideLoRa enhances packet detection capability by 1.7× compared to the state-of-the-art.