InverTwin: Solving Inverse Problems via Differentiable Radio Frequency Digital Twin
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909744395976704 |
|---|---|
| author | Chen, Xingyu Ding, Jianrong Zheng, Kai Fang, Xinmin Zhang, Xinyu Lu, Chris Xiaoxuan Li, Zhengxiong |
| author_facet | Chen, Xingyu Ding, Jianrong Zheng, Kai Fang, Xinmin Zhang, Xinyu Lu, Chris Xiaoxuan Li, Zhengxiong |
| contents | Digital twins (DTs), virtual simulated replicas of physical scenes, are transforming various industries. However, their potential in radio frequency (RF) sensing applications has been limited by the unidirectional nature of conventional RF simulators. In this paper, we present InverTwin, an optimization-driven framework that creates RF digital twins by enabling bidirectional interaction between virtual and physical realms. InverTwin overcomes the fundamental differentiability challenges of RF optimization problems through novel design components, including path-space differentiation to address discontinuity in complex simulation functions, and a radar surrogate model to mitigate local non-convexity caused by RF signal periodicity. These techniques enable smooth gradient propagation and robust optimization of the DT model. Our implementation and experiments demonstrate InverTwin's versatility and effectiveness in augmenting both data-driven and model-driven RF sensing systems for DT reconstruction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14204 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | InverTwin: Solving Inverse Problems via Differentiable Radio Frequency Digital Twin Chen, Xingyu Ding, Jianrong Zheng, Kai Fang, Xinmin Zhang, Xinyu Lu, Chris Xiaoxuan Li, Zhengxiong Signal Processing Digital twins (DTs), virtual simulated replicas of physical scenes, are transforming various industries. However, their potential in radio frequency (RF) sensing applications has been limited by the unidirectional nature of conventional RF simulators. In this paper, we present InverTwin, an optimization-driven framework that creates RF digital twins by enabling bidirectional interaction between virtual and physical realms. InverTwin overcomes the fundamental differentiability challenges of RF optimization problems through novel design components, including path-space differentiation to address discontinuity in complex simulation functions, and a radar surrogate model to mitigate local non-convexity caused by RF signal periodicity. These techniques enable smooth gradient propagation and robust optimization of the DT model. Our implementation and experiments demonstrate InverTwin's versatility and effectiveness in augmenting both data-driven and model-driven RF sensing systems for DT reconstruction. |
| title | InverTwin: Solving Inverse Problems via Differentiable Radio Frequency Digital Twin |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2508.14204 |