Environmental Feature Engineering and Statistical Validation for ML-Based Path Loss Prediction
This work addresses the need for more accurate and scalable propagation modeling in wireless deployments, though it is incremental as it builds on previous feature-based approaches.
The paper tackles the problem of predicting path loss in wireless communications by introducing an extended set of environmental features, resulting in improved prediction accuracy and proven model generalization through statistical validation.
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.