# Towards Generative Ray Path Sampling for Faster Point-to-Point Ray Tracing

arXiv: https://arxiv.org/abs/2410.23773 · HTML: https://mlsanity.com/paper/2410.23773/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 31 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [GFlowNet Foundations](https://mlsanity.com/paper/2111.09266/) (2021)
- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [A Survey on Deep Graph Generation: Methods and Applications](https://mlsanity.com/paper/2203.06714/) (2022)
- [Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling](https://mlsanity.com/paper/2303.11103/) (2023)
- [Generative Augmented Flow Networks](https://mlsanity.com/paper/2210.03308/) (2022)
- [Learning Radio Environments by Differentiable Ray Tracing](https://mlsanity.com/paper/2311.18558/) (2023)
- [Differentiable and Learnable Wireless Simulation with Geometric Transformers](https://mlsanity.com/paper/2406.14995/) (2024)
- [Machine Learning-Based 3D Channel Modeling for U2V mmWave Communications](https://mlsanity.com/paper/2109.02104/) (2021)
- [Min-Path-Tracing: A Diffraction Aware Alternative to Image Method in Ray Tracing](https://mlsanity.com/paper/2301.06399/) (2023)
- [Fully Differentiable Ray Tracing via Discontinuity Smoothing for Radio Network Optimization](https://mlsanity.com/paper/2401.11882/) (2024)
- [Geometry-Based Stochastic Line-of-Sight Probability Model for A2G Channels under Urban Scenarios](https://mlsanity.com/paper/2109.02222/) (2021)
- [Wireless Channel Prediction in Partially Observed Environments](https://mlsanity.com/paper/2207.00934/) (2022)
- [Meta Generative Flow Networks with Personalization for Task-Specific Adaptation](https://mlsanity.com/paper/2306.09742/) (2023)

## Built on by (most cited first, 1 of 1)

- [Transform-Invariant Generative Ray Path Sampling for Efficient Radio Propagation Modeling](https://mlsanity.com/paper/2603.01655/) (2026)

## Abstract (arXiv)

Radio propagation modeling is essential in telecommunication research, as radio channels result from complex interactions with environmental objects. Recently, Machine Learning has been attracting attention as a potential alternative to computationally demanding tools, like Ray Tracing, which can model these interactions in detail. However, existing Machine Learning approaches often attempt to learn directly specific channel characteristics, such as the coverage map, making them highly specific to the frequency and material properties and unable to fully capture the underlying propagation mechanisms. Hence, Ray Tracing, particularly the Point-to-Point variant, remains popular to accurately identify all possible paths between transmitter and receiver nodes. Still, path identification is computationally intensive because the number of paths to be tested grows exponentially while only a small fraction is valid. In this paper, we propose a Machine Learning-aided Ray Tracing approach to efficiently sample potential ray paths, significantly reducing the computational load while maintaining high accuracy. Our model dynamically learns to prioritize potentially valid paths among all possible paths and scales linearly with scene complexity. Unlike recent alternatives, our approach is invariant with translation, scaling, or rotation of the geometry, and avoids dependency on specific environment characteristics.