RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments

TL;DR

RayProNet introduces a neural point field framework utilizing point clouds and spherical harmonics to model radio wave propagation in 3D environments, enabling flexible, scalable, and accurate wireless channel predictions for network planning.

Contribution

It presents a novel machine learning-based method combining point-cloud neural networks and spherical harmonics for improved radio propagation modeling.

Findings

Effective in outdoor and indoor environments

Predicts radio power maps accurately

Supports flexible antenna and location configurations

Abstract

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.