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title: Software
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# Open Source Software
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## RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments

![RayProNet:Workflow](/assets/images/software/RayProNet1.png)

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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 3D 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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![RayProNet:Demonstration](/assets/images/software/RayProNet2.png)

Code (GitHub): [https://github.com/GeCao/neural-point-EM-field ](https://github.com/GeCao/neural-point-EM-field ) \\
Videa (Vimeo): [https://vimeo.com/1096085994](https://vimeo.com/1096085994) \\
Paper DOI: [https://ieeexplore.ieee.org/document/10684152](https://ieeexplore.ieee.org/document/10684152)