Neural Reflectance Fields for Radio-Frequency Ray Tracing

TL;DR

This paper introduces a neural reflectance field approach for RF ray tracing that efficiently learns material reflectivity from signal measurements, improving prediction accuracy with less data in complex environments.

Contribution

It adapts neural reflectance fields from optics to RF signals, modeling amplitude and phase to optimize material reflectivity estimation from path loss data.

Findings

Successfully learns reflection coefficients for all incident angles.

Achieves better prediction accuracy with less training data.

Demonstrates effectiveness in complex simulated environments.

Abstract

Ray tracing is widely employed to model the propagation of radio-frequency (RF) signal in complex environment. The modelling performance greatly depends on how accurately the target scene can be depicted, including the scene geometry and surface material properties. The advances in computer vision and LiDAR make scene geometry estimation increasingly accurate, but there still lacks scalable and efficient approaches to estimate the material reflectivity in real-world environment. In this work, we tackle this problem by learning the material reflectivity efficiently from the path loss of the RF signal from the transmitters to receivers. Specifically, we want the learned material reflection coefficients to minimize the gap between the predicted and measured powers of the receivers. We achieve this by translating the neural reflectance field from optics to RF domain by modelling both the amplitude and phase of RF signals to account for the multipath effects. We further propose a differentiable RF ray tracing framework that optimizes the neural reflectance field to match the signal strength measurements. We simulate a complex real-world environment for experiments and our simulation results show that the neural reflectance field can successfully learn the reflection coefficients for all incident angles. As a result, our approach achieves better accuracy in predicting the powers of receivers with significantly less training data compared to existing approaches.