InverTwin: Solving Inverse Problems via Differentiable Radio Frequency Digital Twin

TL;DR

InverTwin introduces a novel framework for creating differentiable RF digital twins that enable bidirectional interaction and robust optimization, enhancing RF sensing applications.

Contribution

It presents innovative differentiability techniques and a radar surrogate model to improve RF digital twin creation and optimization.

Findings

Successfully enables bidirectional RF digital twin interaction.

Improves robustness of RF signal modeling and optimization.

Enhances RF sensing system performance with digital twins.

Abstract

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.