From Waves to Graphs: A Ray-Tracing-Inspired Neural Radio Propagation Model
This model provides a faster and more robust solution for mobile network operators to optimize wireless network performance and expansion, particularly for inferring radio-related quantities in 3D environments.
This paper introduces GRAPHWAVE, a neural graph-driven radio propagation model inspired by ray tracing. It uses a digitized environment to create a point cloud and graph, then applies neural message passing to infer radio quantities like received signal strength in 3D environments.
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.