Path Loss Prediction Using Machine Learning with Extended Features

2025-01-14

AI Summary

To address the poor generalizability and limited accuracy of conventional path loss models, this paper proposes a machine learning–based modeling approach that integrates multi-source GIS geospatial data with propagation geometry features. We innovatively construct an extended feature set encompassing terrain elevation, building height and material properties, street width, and three-dimensional propagation angles. Supervised learning algorithms—including XGBoost and Random Forest—are employed to develop a high-accuracy path loss prediction model. Evaluated on multi-region measurement datasets, the proposed model achieves a 32% reduction in mean absolute error compared to the Okumura-Hata and ITU-R P.1546 models. It demonstrates significantly improved cross-domain generalization across urban, suburban, and indoor environments while maintaining computational efficiency suitable for large-scale wireless network planning.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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 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.

Problem

• Machine Learning
• Path Loss Model
• Wireless Communication

Innovation

• Machine Learning
• Geographic Information System
• Path Loss Modeling