Physically Accurate Differentiable Inverse Rendering for Radio Frequency Digital Twin
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914408280621056 |
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| author | Chen, Xingyu Zhang, Xinyu Zheng, Kai Fang, Xinmin Li, Tzu-Mao Lu, Chris Xiaoxuan Li, Zhengxiong |
| author_facet | Chen, Xingyu Zhang, Xinyu Zheng, Kai Fang, Xinmin Li, Tzu-Mao Lu, Chris Xiaoxuan Li, Zhengxiong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_18026 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Physically Accurate Differentiable Inverse Rendering for Radio Frequency Digital Twin Chen, Xingyu Zhang, Xinyu Zheng, Kai Fang, Xinmin Li, Tzu-Mao Lu, Chris Xiaoxuan Li, Zhengxiong Signal Processing Graphics Machine Learning 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. |
| title | Physically Accurate Differentiable Inverse Rendering for Radio Frequency Digital Twin |
| topic | Signal Processing Graphics Machine Learning |
| url | https://arxiv.org/abs/2603.18026 |