CurveALOHA: Non-linear Chirps Enabled High Throughput Random Channel Access for LoRa

Long Range Wide Area Network (LoRaWAN), using the linear chirp for data modulation, is known for its low-power and long-distance communication to connect massive Internet-of-Things devices at a low cost. However, LoRaWAN throughput is far behind the demand for the dense and large-scale IoT deployments, due to the frequent collisions with the by-default random channel access (i.e., ALOHA). Recently, some works enable an effective LoRa carrier-sense for collision avoidance. However, the continuous back-off makes the network throughput easily saturated and degrades the energy efficiency at LoRa end nodes. In this paper, we propose CurveALOHA, a brand-new media access control scheme to enhance the throughput of random channel access by embracing non-linear chirps enabled quasi-orthogonal logical channels. First, we empirically show that non-linear chirps can achieve similar noise tolerance ability as the linear one does. Then, we observe that multiple nonlinear chirps can create new logical channels which are quasi-orthogonal with the linear one and each other. Finally, given a set of non-linear chirps, we design two random chirp selection methods to guarantee an end node can access a channel with less collision probability. We implement CurveALOHA with the software-defined radios and conduct extensive experiments in both indoor and outdoor environments. The results show that CurveALOHA’s network throughput is 59.6% higher than the state-of-the-art carrier-sense MAC.