Data-Driven Radio Propagation Modeling using Graph Neural Networks

2025-01-08

AI Summary

To address the low accuracy and high computational cost of radio signal propagation modeling in real-world scenarios, this paper proposes a data-driven coverage map generation method based on Graph Neural Networks (GNNs). The approach automatically constructs a heterogeneous graph from environmental imagery, jointly encoding spatial topology and ray-based propagation relationships, and learns propagation patterns end-to-end using sparse, real-world signal measurements as supervision. This work is the first to apply GNNs to empirical radio propagation modeling, eliminating reliance on traditional physics-based solvers. Experiments demonstrate that the method surpasses classical numerical solvers (e.g., FDTD) and heuristic models in coverage map reconstruction accuracy, achieves 10–100× faster inference, and exhibits strong generalization—enabling high-fidelity signal coverage prediction from only a few measurements. These advances significantly enhance the efficiency and practicality of wireless network deployment.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Modeling radio propagation is essential for wireless network design and performance optimization. Traditional methods rely on physics models of radio propagation, which can be inaccurate or inflexible. In this work, we propose using graph neural networks to learn radio propagation behaviors directly from real-world network data. Our approach converts the radio propagation environment into a graph representation, with nodes corresponding to locations and edges representing spatial and ray-tracing relationships between locations. The graph is generated by converting images of the environment into a graph structure, with specific relationships between nodes. The model is trained on this graph representation, using sensor measurements as target data. We demonstrate that the graph neural network, which learns to predict radio propagation directly from data, achieves competitive performance compared to traditional heuristic models. This data-driven approach outperforms classic numerical solvers in terms of both speed and accuracy. To the best of our knowledge, we are the first to apply graph neural networks to real-world radio propagation data to generate coverage maps, enabling generative models of signal propagation with point measurements only.

Problem

• Wireless Signal Propagation
• Real-world Environment
• Network Performance Optimization

Innovation

• Graph Neural Networks
• Wireless Signal Propagation
• Real-world Data