An Efficient Ray-Tracing Framework for Urban Scenarios Powered by Heterogeneous Graph Neural Networks

Abstract

In this letter, we propose an accurate and efficient heterogeneous graph neural network-powered ray tracing (HGNN-RT) framework to predict path loss for urban scenarios. The proposed HGNN-RT effectively captures both global and local features, which include broad path attributes, such as total path length and line-of-sight conditions, as well as specific interactions, such as reflection angles and obstacles. Moreover, due to the fast information flow of the graph neural networks, the HGNN-RT achieves efficiency-improved path loss prediction in complex multireflection and multipath propagation channels. Experimental results demonstrate that the proposed approach achieves errors within 2 dB of ray tracing benchmarks, while also being time-efficient and reliably generalizing to unseen environments for channel modeling.