RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments
This addresses the need for efficient wireless network planning and deployment optimization, though it appears incremental as it builds on existing neural and point-cloud techniques.
The paper tackles the problem of modeling radio wave propagation in 3D environments for wireless communication systems by introducing a neural point field framework, resulting in a flexible and scalable method validated in various settings.
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.