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

TL;DR

This paper introduces a Machine Learning-assisted method for Ray Tracing in radio propagation modeling, significantly reducing computational costs by efficiently sampling potential paths while maintaining accuracy and invariance to scene transformations.

Contribution

The paper presents a novel ML-aided Ray Tracing approach that dynamically prioritizes valid paths, scales linearly with scene complexity, and is invariant to geometric transformations.

Findings

Reduces Ray Tracing computational load significantly

Maintains high accuracy in path identification

Scales linearly with scene complexity

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.