Joint multidimensional features for LoRa reception in burst traffic

Abstract

LoRa has gained significant attention as a promising communication technology in the IoT field. However, with the widespread use of LoRa, network performance faces challenges due to signal collisions at base stations during concurrent transmissions. Traditional methods rely on signal characteristics like frequency to separate colliding packets but have limitations in burst traffic scenarios. These methods fail to accurately separate signals due to unstable and insufficiently detailed signal features. In this paper, we propose a novel physical layer approach called SCLoRa, which can decode overlapping LoRa signals that have collided. SCLoRa utilizes cumulative spectral coefficients, combining frequency and power spectral density, to identify symbols in overlapping signals. This approach takes into account practical factors such as channel fading, symbol boundary alignment, and spectral leakage, which are crucial for accurate signal separation. Enhanced Dynamic-Window and Reuse-Window designs are introduced to further improve decoding reliability and reduce the computational cost. We implement SCLoRa on USRP B210 base stations and standard LoRa nodes (e.g., SX1278). Experiments across various scenarios and radio parameter configurations show that SCLoRa achieves a $3\times$ throughput improvement compared to state-of-the-art methods.