Nonlinear Chirp Spread Spectrum: Performance Analysis and Optimization for LoRa Networks

LoRa has emerged as a crucial technology that provides ubiquitous connectivity for geographically distributed Internet of Things devices. LoRa employs linear Chirp Spread Spectrum (CSS) modulation in its physical layer to enable long-range communication. Recently, nonlinear chirps have been proposed to replace linear chirps in CSS, to enhance the capacity and scalability of LoRa technology. However, the characteristics and advantages of nonlinear chirps are not yet fully understood. To unleash the potential of nonlinear CSS modulation, this paper presents a comprehensive theoretical and experimental analysis of the nonlinear chirp, focusing on key aspects such as noise resilience, error correction, curvature selection, and bandwidth expansion. Our findings reveal that nonlinear chirps are intrinsically more resilient to noise than linear chirps. Moreover, nonlinear chirps typically exhibit a single-place demodulation error under strong noise conditions, which has informed our development of a simple yet effective error correction design. Additionally, users should carefully manage nonlinear chirp curvatures and signal power compensation to avoid distortion while benefiting from the bandwidth expansion offered by nonlinear chirps. Building on these insights, we further refine nonlinear CSS modulation and implement our optimized design on US-RPs. Field experiments demonstrate that our design achieves significant performance improvements, marking a step forward in the practical application of nonlinear CSS modulation in LoRa networks.