The radio wave propagation channel is central to the performance of wireless communication systems. In this paper, we introduce a novel machine learning-empowered methodology for wireless channel modeling. The key ingredients include a point-cloud-based neural network and a Spherical Harmonics encoder with light probes. Our approach offers several significant advantages, including the flexibility to adjust antenna radiation patterns and transmitter/receiver locations, the capability to predict radio power maps, and the scalability of large-scale wireless scenes. As a result, it lays the groundwork for an end-to-end pipeline for network planning and deployment optimization. The proposed work is validated in various outdoor and indoor radio environments.
@article{arxiv.2406.16907,
title = {RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments},
author = {Ge Cao and Zhen Peng},
journal= {arXiv preprint arXiv:2406.16907},
year = {2024}
}