Performance Analysis of Coherent and Non-coherent Detection Techniques in Chirp Spread Spectrum for Internet of Things Applications

This paper presents an analysis of Chirp Spread Spectrum (CSS) for Internet of Things (IoT) applications, focusing on the performance of coherent and non-coherent detectors in various channel environments. The study evaluates scenarios with additive white Gaussian noise (AWGN), frequency-selective channels, and Rayleigh fading channels, considering a system with 10 receiving antennas. The results demonstrate that the coherent detector outperforms the non-coherent detector in terms of performance. However, the non-coherent detector offers the advantage of lower complexity. To further improve the performance of non-coherent detection in CSS-based IoT systems, the need for exploring new techniques is emphasized. Future research should aim to bridge the performance gap between coherent and non-coherent detection, considering adaptive signal processing algorithms, advanced filtering techniques, or hybrid detection schemes. By addressing the challenges associated with non-coherent detection, CSS can become a reliable and efficient modulation scheme for low signal-to-noise ratio conditions in IoT applications. This research contributes to the advancement of CSS in IoT, enabling seamless connectivity and data exchange in diverse IoT scenarios.