mlsanityData to 7 Oct 2026

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

arXiv 2406.16907 ↗First posted 4 Jun 2024eess.SP cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 21 of its 71 references that are in the map. First posted on arXiv 4 Jun 2024.

Builds on

21 of 71 references.

References · 21

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9 more
  1. 2021Neural Point Light Fieldscited by 51
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2026Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Surveycited by 3
  2. 2025Photon Splatting: A Physics-Guided Neural Surrogate for Real-Time Wireless Channel Predictioncited by 0
  3. 2026Transform-Invariant Generative Ray Path Sampling for Efficient Radio Propagation Modelingcited by 0
  4. 2026Point2Radio: A Foundation Model for Cross-Scene Radio Fields from Material-Aware Point Cloudscited by 0