Physically Accurate Differentiable Inverse Rendering for Radio Frequency Digital Twin
This work addresses the limitation of digital twins in RF systems for industries like sensing and communications, representing a novel method for a known bottleneck.
The paper tackles the problem of non-differentiable radio frequency (RF) simulators for digital twins by introducing RFDT, a physically based differentiable framework that resolves discontinuities and non-convexity, enabling accurate reconstruction of digital twins from real RF measurements and augmentation of downstream applications like RF sensing and communication optimization.
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.