Physically Accurate Differentiable Inverse Rendering for Radio Frequency Digital Twin

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.

Figures (22)

• Figure 1: RFDT constructs digital twins by solving the inverse problem of RF simulation. Top: 3D reconstruction through radar simulation. Bottom: Communication system optimization through RF scene rendering.
• Figure 2: Reparametrized RF Ray Tracing and RFDT Secondary Visibility.
• Figure 3: Wedge discontinuity as Rx location translates from $P_\text{rx}^0$ to $P_\text{rx}^t$. $\mathcal{S}$: continuity of changes in signal strength. $\mathcal{C}$: total energy conservation during translation. Conventional: discontinuous step. Softened triangle: heuristic sigmoid, physically incorrect. Ours: continuous and physically correct---As the reflection point approaches the edge, the purely reflection contribution is smoothly redistributed into a diffraction component, as captured by the transition function $\mathcal{F}(\cdot)$.
• Figure 4: RFDT's surrogate model provides a smoother loss landscape for faster convergence in the early stages, while progressively annealing to the conventional signal-domain transform for unbiased results.
• Figure 5: RFDT forward simulation and backward propagation flow.