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

• URL: http://arxiv.org/abs/2406.16907v1
• Date: Tue, 4 Jun 2024 01:06:41 GMT
• Title: RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments
• Authors: Ge Cao, Zhen Peng,
• Abstract summary: We introduce a novel machine learning-empowered methodology for wireless channel modeling. Key ingredients include a point-cloud-based neural network and a Spherical Harmonics encoder with light probes.
• Score: 1.7074276434401858
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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