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.

翻译：随着物联网技术的快速发展，亚GHz免许可频段正日益被远距离通信协议（如LoRa、Sigfox和远距离跳频扩频）所共享。这些协议必须在相同频段内共存，从而导致相互干扰。本文研究了两种窄带信号（BPSK和GMSK）对LoRa解调的物理层影响。我们采用符号级蒙特卡洛仿真，分析了在给定信噪比和噪声基底条件下，干扰噪声比对符号错误率的影响，并将结果与等功率加性高斯白噪声下的结果进行比较。研究表明，将窄带干扰建模为加性高斯白噪声会系统性地高估啁啾扩频解调的符号错误率。我们还阐明了加性高斯白噪声与两类窄带干扰源所引起的不同损伤程度，并深入揭示了其内在物理机制。最后，我们拟合了一个两段函数，用于确定确保在不同信噪比下实现正确解调的最大干扰噪声比，其中一段适用于低信噪比场景，另一段适用于高信噪比场景。