GeNeRT: A Physics-Informed Approach to Intelligent Wireless Channel Modeling via Generalizable Neural Ray Tracing

TL;DR

GeNeRT introduces a physics-informed neural ray tracing framework that significantly improves generalization, accuracy, and efficiency in wireless channel modeling across various outdoor scenarios, surpassing existing methods.

Contribution

The paper presents GeNeRT, a novel neural ray tracing approach with Fresnel-inspired design and GPU acceleration, enabling zero-shot generalization and higher MPC prediction accuracy.

Findings

Effective zero-shot generalization across unseen environments.

Superior MPC prediction accuracy compared to baseline models.

Enhanced runtime efficiency over Wireless Insite in multi-transmitter scenarios.

Abstract

Neural ray tracing (RT) has emerged as a promising paradigm for channel modeling by combining physical propagation principles with neural networks. It enables high modeling accuracy and efficiency. However, current neural RT methods face two key limitations: constrained generalization capability due to strong spatial dependence, and weak adherence to electromagnetic laws. In this paper, we propose GeNeRT, a Generalizable Neural RT framework with enhanced generalization, accuracy and efficiency. GeNeRT supports both intra-scenario spatial transferability and inter-scenario zero-shot generalization. By incorporating Fresnel-inspired neural network design, it also achieves higher accuracy in multipath component (MPC) prediction. Furthermore, a GPU-tensorized acceleration strategy is introduced to improve runtime efficiency. Extensive experiments conducted in outdoor scenarios demonstrate that GeNeRT generalizes well across untrained regions within a scenario and entirely unseen environments, and achieves superior accuracy in MPC prediction compared to baselines. Moreover, it outperforms Wireless Insite in runtime efficiency, particularly in multi-transmitter settings. Ablation experiments validate the effectiveness of the network architecture and training strategy in capturing physical principles of ray-surface interactions.