ChirpTransformer: Versatile LoRa Encoding for Low-power Wide-area IoT

This paper introduces ChirpTransformer, a versatile LoRa encoding framework that harnesses broad chirp features to dynamically modulate data, enhancing network coverage, throughput, and energy efficiency. Unlike the standard LoRa encoder that offers only single configurable chirp feature, our framework introduces four distinct chirp features, expanding the spectrum of methods available for data modulation. To implement these features on commercial off-the-shelf (COTS) LoRa nodes, we utilize a combination of a software design and a hardware interrupt. ChirpTransformer serves as the foundation for optimizing encoding and decoding in three specific case studies: weak signal decoding for extended network coverage, concurrent transmission for heightened network throughput, and data rate adaptation for improved network energy efficiency. Each case study involves the development of an end-to-end system to comprehensively evaluate its performance. The evaluation results demonstrate remarkable enhancements compared to the standard LoRa. Specifically, ChirpTransformer achieves a 2.38 x increase in network coverage, a 3.14 x boost in network throughput, and a 3.93 x of battery lifetime.