提出可微分的射频数字孪生框架，实现物理世界与虚拟世界的梯度交互。
Physically Accurate Differentiable Inverse Rendering for Radio Frequency Digital Twin
- 基于物理规律设计可微分射频仿真，解决路径可见性导致的不连续问题。
- 通过信号域替代傅里叶域处理，缓解非凸优化难题，提升重建精度。
- 适用于射频感知模型在线适应与通信系统物理约束优化。
数字孪生作为物理场景的虚拟模拟副本，正在重塑各行业的系统设计。然而，传统射频（RF）模拟器的不可微特性限制了其在射频领域的应用。传播路径的可见性引发严重不连续性，而计算机图形学中的可微渲染技术难以直接迁移，因点源天线和主导的镜面反射。本文提出RFDT，一种基于物理的可微射频仿真框架，实现虚拟与物理世界间的梯度交互。RFDT采用物理基础的边缘绕射过渡函数解决不连续性，并通过信号域变换代理缓解傅里叶域处理带来的非凸性。实验表明，RFDT能准确从真实射频测量中重建数字孪生。此外，该框架可拓展至多种下游应用，如基于机器学习的射频感知测试时自适应及通信系统的物理约束优化。
原文摘要 · Abstract (English)
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.
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