Received signal strength (RSS) is a key metric for assessing the strength of a received signal. However, indoor signal stability is affected by multiple factors, leading to RSS fluctuations even at fixed locations. These variations necessitate a more adaptive calculation method for complex scenarios. To address this, we propose a novel method for accurate signal propagation and RSS prediction. It adjusts these parameters based on actual measurement data from various indoor scenarios to support accurate indoor signal calculations. Additionally, it divides the signal calculation process into short‐distance and long‐distance segments, allowing for faster signal attenuation at close ranges and slower decay at further distances. Experimental results across diverse indoor environments demonstrate that our approach significantly outperforms traditional models, reducing the root mean squared error on average by 82.37 percent, from 25.64 to 4.52 dBm, and achieving higher prediction accuracy and robustness. This model offers a scalable and adaptive solution for optimizing wireless network performance in diverse and dynamic indoor settings.
Liang et al. (Thu,) studied this question.