Physically Accurate Differentiable Inverse Rendering for Radio Frequency Digital Twin

TL;DR

This paper introduces RFDT, a novel differentiable RF simulation framework that enables gradient-based optimization and accurate digital twin reconstruction for RF systems, overcoming traditional non-differentiability issues.

Contribution

RFDT is the first physically grounded differentiable RF simulator that handles discontinuities and non-convexities, facilitating advanced RF system design and optimization.

Findings

Accurately reconstructs RF digital twins from real measurements

Enables gradient-based RF sensing and system optimization

Mitigates discontinuities and non-convexities in RF simulation

Abstract

Digital twins, virtual simulated replicas of physical scenes, are transforming system design across industries. However, their potential in radio frequency (RF) systems has been limited by the non-differentiable nature of conventional RF simulators. The visibility of propagation paths causes severe discontinuities, and differentiable rendering techniques from computer graphics cannot easily transfer due to point-source antennas and dominant specular reflections. In this paper, we present RFDT, a physically based differentiable RF simulation framework that enables gradient-based interaction between virtual and physical worlds. RFDT resolves discontinuities with a physically grounded edge-diffraction transition function, and mitigates non-convexity from Fourier-domain processing through a signal domain transform surrogate. Our implementation demonstrates RFDT's ability to accurately reconstruct digital twins from real RF measurements. Moreover, RFDT can augment diverse downstream applications, such as test-time adaptation of machine learning-based RF sensing and physically constrained optimization of communication systems.