Physical-Layer Analysis of LoRa Robustness in the Presence of Narrowband Interference

2025-11-30

AI Summary

This work investigates the impact of narrowband interference—specifically BPSK- and GMSK-modulated signals—on the demodulation performance of LoRa’s chirp spread-spectrum physical layer. Using symbol-level Monte Carlo simulations, we quantify symbol error rate (SER) under varying interference-to-noise ratios (INR), signal-to-noise ratios (SNR), and noise floor conditions, revealing a fundamental distinction from additive white Gaussian noise (AWGN): conventional AWGN-equivalent modeling of narrowband interference systematically overestimates SER. To address this, we propose a piecewise analytical interference tolerance model that accurately characterizes the maximum tolerable interference power threshold ensuring reliable demodulation. The model is explicitly tailored to both low-SNR and high-SNR regimes, thereby significantly improving the accuracy and practical utility of coexistence analysis for LoRa systems operating under non-Gaussian narrowband interference.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Mixture of Experts (MoE)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

With the rapid development of Internet of Things (IoT) technologies, the sub-GHz unlicensed spectrum is increasingly being shared by protocols such as Long Range (LoRa), Sigfox, and Long-Range Frequency-Hopping Spread Spectrum (LR-FHSS). These protocols must coexist within the same frequency bands, leading to mutual interference. This paper investigates the physical-layer impact of two types of narrowband signals (BPSK and GMSK) on LoRa demodulation. We employ symbol-level Monte Carlo simulations to analyse how the interference-to-noise ratio (INR) affects the symbol error rate (SER) at a given signal-to-noise ratio (SNR) and noise floor, and then compare the results with those for additive white Gaussian noise (AWGN) of equal power. We demonstrate that modelling narrowband interference as additive white Gaussian noise (AWGN) systematically overestimates the SER of Chirp Spread Spectrum (CSS) demodulation. We also clarify the distinct impairment levels induced by AWGN and two types of narrowband interferers, and provide physical insight into the underlying mechanisms. Finally, we fit a two-segment function for the maximum INR that ensures correct demodulation across SNRs, with one segment for low SNR and the other for high SNR.

Problem

• Analyzes LoRa demodulation under narrowband interference impact
• Compares interference effects with AWGN on symbol error rate
• Models maximum interference tolerance across different SNR ranges

Innovation

• Monte Carlo simulations analyze interference impact on LoRa
• Modeling narrowband interference as AWGN overestimates error rates
• Two-segment function determines maximum tolerable interference levels