AntScale: Extracting Target LoRa Packets for Cross-channel Collisions Through Antithetical -stationary Scaling

Owing to its ultra-low power consumption and long-range communication capabilities, LoRa, a prominent Low-Power Wide Area Network (LPWAN) technology, has seen widespread deployment across academic and industrial domains. However, its operation in unlicensed spectrum and reliance on ALOHA-based Medium Access Control (MAC) make it highly susceptible to packet collisions, undermining network reliability and performance. While existing efforts primarily address intra-channel collisions, the effects of cross-channel interference—caused by overlapping transmissions using different bandwidths or spreading factors—remain insufficiently studied, posing challenges to communication quality, scalability, and spectrum efficiency. To bridge this gap, we propose AntScale, a novel collision resolution framework based on antithetical - stationary scaling. Specifically, AntScale introduces two key techniques: (1) it transforms minor time-domain signal distortions into robust frequency-domain features through dual antithetical scaling, enabling effective collision suppression; and (2) it employs a patching algorithm that leverages wide-bandwidth signal characteristics to identify and isolate dominant interference components for targeted decoding. Implementation on a USRP N210 platform demonstrates that AntScale achieves a 2.3× throughput improvement over state-of-the-art methods.