Path Loss Prediction Using Machine Learning with Extended Features

Wireless communications rely on path loss modeling, which is most effective when it includes the physical details of the propagation environment. Acquiring this data has historically been challenging, but geographic information system data is becoming increasingly available with higher resolution and accuracy. Access to such details enables propagation models to more accurately predict coverage and minimize interference in wireless deployments. Machine learning-based modeling can significantly support this effort, with feature-based approaches allowing for accurate, efficient, and scalable propagation modeling. Building on previous work, we introduce an extended set of features that improves prediction accuracy while, most importantly, maintaining model generalization across a broad range of environments.

Machine Learning-Based Path Loss Modeling with Simplified Features

Propagation modeling is a crucial tool for successful wireless deployments and spectrum planning with the demand for high modeling accuracy continuing to grow. Recognizing that detailed knowledge of the physical environment (terrain and clutter) is essential, we propose a novel approach that uses environmental information for predictions. Instead of relying on complex, detail-intensive models, we explore the use of simplified scalar features involving the total obstruction depth along the direct path from transmitter to receiver. Obstacle depth offers a streamlined, yet surprisingly accurate, method for predicting wireless signal propagation, providing a practical solution for efficient and effective wireless network planning.