Exploiting PHY for improving LoRa based communication and localisation system

LoRa is an emerging technology of low-power wide-area networks (LPWANs) operating on industrial, scientific and medical (ISM) bands to provide connectivity for Internet of Thing (IoT) devices. As the number of devices increases, the network suffers from scalability issues. Therefore, we design a cloud radio access network (C-RAN or Cloud-RAN) with multiple LoRa gateways to solve this problem. Furthermore, we develop novel algorithms to provide accurate localisation for LoRa devices. This thesis makes three new contributions to LoRa based communication and localisation system as follows. The first contribution is a compressive sensing-based algorithm to reduce the uplink bit rate between the gateways and the cloud server. The proposed novel compression algorithm can reduce the bandwidth usage for the fronthaul without decreasing LoRa packet delivery rates. Our evaluation shows that with four gateways up to 87.5% PHY samples can be compressed and 1.7x battery life for end devices can be achieved. The second contribution is a novel algorithm to improve the resolution of the radio signals for localisation. The proposed algorithm synchronises multiple non-overlapped communication channels by exploiting the unique features of the LoRa radio to increase the overall bandwidth. We evaluate its performance in an outdoor area of 100 m × 60 m, which shows a median error of 4.4 m, and a 36.2% error reduction compared to the baseline. The above approach improves the accuracy of outdoor localisation; however, it does not work for indoor localisation due to the increase of multiple radio propagation paths. Therefore, our third contribution is an improved super-resolution algorithm for indoor localisation. By exploiting both the original and the conjugate of the physical layer, the algorithm can resolve the multiple paths from multiple reflectors in clustered indoor environments. We evaluate its performance in an indoor area of 25 m × 15 m, which shows that a median error of 2.4 m can be achieved, which is 47.8% and 38.5% less than the baseline approach and the approach without using the conjugate information, respectively. Our evaluation also shows that, different to previous studies in Wi-Fi localisation systems that have significantly wider bandwidth, time-of-fight (ToF) estimation is less effective to LoRa localisation systems with narrowband radio signals