LAUSR

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.