Enabling Concurrency for Non-orthogonal LoRa Channels

Existing LoRa only supports the concurrency of orthogonal channels but ignores the large number of non-orthogonal channel concurrency opportunities. In this paper, we propose Mc-LoRa that enables LoRa concurrency for non-orthogonal overlapping channels by solving cross-channel collision that happens when chirps with different bandwidths have the same slope in time-frequency domain. Existing single-channel concurrency methods fail to resolve this new collision because the deterministic symbol offset is invalid anymore due to the asymmetric symbol duration. But we find that when wiping a part of collided signals, the amplitude change of target chirp that aligns with the decoding window is predictable, while the collided chirps experience different changes. We accordingly regard the amplitude change ratio before and after wiping as a new decoding feature. We propose a wiper selection method based on our theoretical model to obtain robust features. We also design noise-aware wiper searching and grouping mechanisms to balance the feature accuracy and computing overhead. The experiments show that Mc-LoRa efficiently decodes packets in non-orthogonal overlapping channels and improves the network throughput by up to 3.4× under cross-channel collision, compared with the state-of-the-art single-channel concurrency methods.