Environmental Feature Engineering and Statistical Validation for ML-Based Path Loss Prediction

TL;DR

This paper enhances machine learning-based path loss prediction by incorporating detailed environmental features and validating model generalization through rigorous statistical methods, leveraging high-resolution geographic data.

Contribution

It introduces an extended feature set for ML models and demonstrates improved accuracy and generalization with statistical validation techniques.

Findings

Enhanced feature set improves prediction accuracy.

Rigorous statistical validation confirms model generalization.

High-resolution geographic data benefits path loss modeling.

Abstract

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 systems data is becoming increasingly available with higher resolution and accuracy. Access to such details enables propagation models to more accurately predict coverage and account for 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, proving model generalization through rigorous statistical assessment and the use of test set holdouts.