Hi2LoRa: Exploring Highly Dimensional and Highly Accurate Features to Push LoRaWAN Concurrency Limits with Low Implementation Cost

LoRa Wide Area Network (LoRaWAN) has emerged as a dominant technology for Low Power Wide Area Networks (LPWAN). However, due to the ever-growing network size, packet collisions caused by concurrent transmissions have become a serious challenge in LoRa Wan.Existing studies have either ignored the issue by exploring only a few inaccurate features or addressed it using a complex receiver with up to eight antennas. To strike a better balance between implementation cost and system performance, we propose Hi²LoRa, which leverages highly dimensional and highly accurate features for LoRa concurrent decoding with only two receiving antennas. The feature dimensions are extended by exploring various types of hardware imperfections and channel state information inherent to each transceiver pair. To improve feature accuracy, low pass filters and BiLSTM networks are employed to trace and learn their temporal patterns. Additionally, an effective collision suppression strategy is introduced to combat feature corruption from other concurrent packets. Extensive evaluations on real-world testbeds show that the achievable concurrency in Hi2LoRa is either close to that of state-of-the-art approaches with much higher complexity (e.g., using eight antennas) or 2.7 x of prior work with comparable complexity (e.g., using two antennas).