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

2024-10-31

arXiv.org

AI Summary

In point-to-point ray tracing, conventional path search suffers from exponential computational complexity and extremely sparse effective paths. Method: This paper proposes a machine learning–driven generative ray-path sampling method—the first to introduce generative modeling into ray-path space. We design a geometrically invariant graph neural network (robust to translation, scaling, and rotation) jointly optimized with differentiable path prioritization, enabling end-to-end trainable dynamic focused sampling. Contribution/Results: Our approach eliminates exhaustive enumeration, reducing computational complexity from exponential to linear. It achieves over 92% path selection accuracy without relying on specific frequency bands or material parameters, and maintains high-fidelity channel modeling across diverse propagation scenarios.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Efficient ML / Green AI
• Intelligent Robots: Motion and Path Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Efficient sampling of ray paths
• Reducing computational load in Ray Tracing
• Machine Learning-aided path prioritization

Innovation

• Machine Learning-aided Ray Tracing
• Dynamic path prioritization learning
• Invariance to geometric transformations