RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments
<|reference_start|>RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments: 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.<|reference_end|>
@article{cao2024raypronet:,
    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},
    archivePrefix={arXiv},
    eprint={2406.16907},
    primaryClass={eess.SP cs.LG}
}