Point Cloud-Based Environmental Material Classification for Wireless Channel Ray-Tracing Simulations

Ray-tracing (RT) channel simulation has been widely used for simulating and analyzing propagation of electromagnetic waves in complex environments. Accuracy of RT simulations depends on environment construction, including both scene structure and material classification. However, existing RT studies rely on manual material segmentation and presuppose idealized material parameters, which highly overlooks critical challenges of material recognition from real-world data. This prevents RT from being applied in complex scenarios and leads to inaccurate simulation results. To address this issue, we use point cloud measurements to capture real-world environment information and propose a dual-branch network based on PointNet model to automatically classify environmental materials by integrating point cloud data and LiDAR-derived feature parameters. The proposed network significantly enhances material classification accuracy within complex scenes, thereby delivering more precise and computationally efficient input data for RT simulations. Furthermore, we analyze influence of material recognition accuracy on simulation parameters, such as path loss and delay spread. The results demonstrate that the proposed network achieves high classification performance and meets accuracy requirements of RT, thereby contributing to more realistic and reliable predictions for wireless systems. This approach lays a crucial foundation for development of environment-aware models for 6G networks, enabling more effective simulation of outdoor communication environments.