Environment Features-Based Model for Path Loss Prediction

作者：Yutong Sun, Jianhua Zhang, Yuxiang Zhang, Li Yu, Zhiqiang Yuan, Guangyi Liu, Qixing Wang · 发表于：IEEE Wireless Communications Letters · 年份：2022 · DOI：10.1109/lwc.2022.3192516 · 被引用次数：53 · 研究领域：Millimeter-Wave Propagation and Modeling、Telecommunications and Broadcasting Technologies、Power Line Communications and Noise

Conventionally statistical path loss models are high-dimensional data-based without utilizing specific environment features. In this letter, a novel environment features-based model (EFBM) for path loss prediction is presented. We connect the propagation environment and channel by representing the environment with low-dimensional features: distance, deviation, volume, and blockage. The features are propagation-related, which can predict path loss directly by utilizing the Random Forest (RF) method. Compared with the data-based method, the proposed method can reduce the Root Mean Squared Error (RMSE) by 0.33 and 0.89 dB at 6 and 28 GHz and provide closer results to the Ray-Tracing (RT)-based ground-truth values.