From Waves to Graphs: A Ray-Tracing-Inspired Neural Radio Propagation Model

2026-05-28

AI Summary

This work addresses the high computational complexity of conventional ray tracing methods, which hinders real-time wireless network modeling. The authors propose a novel approach that integrates the physical principles of ray tracing with graph neural networks by converting environmental point clouds into graph structures and leveraging neural message passing to efficiently infer propagation parameters such as signal strength in three-dimensional space. This method establishes the first learnable and generalizable digital twin model of radio environments, enabling joint training on both simulated and real-world measurement data. It achieves high prediction accuracy while significantly reducing inference time, making it well-suited for efficient modeling and prediction in complex 3D wireless scenarios.

Abstract

Artificial intelligence-driven radio propagation models provide agile and robust solutions for mobile network operators in their effort to ensure the optimal performance of the wireless ecosystem and support its efficient expansion. In this paper, we introduce GRAPHWAVE, a neural graph-driven propagation solver hinging on the governing principles of ray tracing. The proposed model leverages a digitized version of the propagation environment to build a point cloud and extract an equivalent graph representation of the radio environment. By applying neural message passing over the equivalent graph, it allows the model to accurately infer radio-related quantities, e.g., received signal strength, in a three-dimensional environment. We showcase the use of GRAPHWAVE as a radio environment digital twin and we demonstrate that the model can learn from synthetic and real-world data while achieving low inference times.

Problem

• radio propagation
• neural modeling
• 3D environment
• signal strength prediction
• wireless networks

Innovation

• neural graph propagation
• ray tracing
• digital twin
• message passing
• point cloud