Prism: High-throughput LoRa Backscatter with Non-linear Chirps

LoRa backscatter enables long-distance communication with ultra-low energy consumption. Enabling concurrent transmissions among many LoRa backscatter tags is desirable for large-scale backscatter networks. However, LoRa backscatter signals sitting on linear chirps easily interfere with each other degrading the throughput of concurrent transmissions. In this paper, we propose Prism that utilizes different types of non-linear chirps to modulate backscatter data allowing multiple backscatter tags to transmit concurrently in the same channel. By taking linear chirps from commercial-off-the-shelf (COTS) LoRa nodes as excitation sources, how to convert the linear chirps to their non-linear counterparts is not trivial on resource-limited backscatter tags. To solve this challenge, we design a lightweight and low-power method, including a frequency-shift function and hardware framework, to shift the frequency of the linear chirps to the non-linear chirps accurately. Moreover, we develop effective methods to calibrate various offsets and concentrate chirp energy to achieve reliable decoding. We implement Prism with customized low-cost hardware, process backscatter signals with USRP, and evaluate its performance in both indoor and outdoor environments. The results show that seven tags can transmit concurrently with less than 1% bit error rate by using seven different types of non-linear chirps in the same channel, resulting in a 6× higher transmission concurrency than state-of-the-art.