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RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments
{:.textCol} 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 3D 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.
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Code (GitHub): https://github.com/GeCao/neural-point-EM-field \ Videa (Vimeo): https://vimeo.com/1096085994 \ Paper DOI: https://ieeexplore.ieee.org/document/10684152