TRACE: Learning to Self-Calibrate Wireless Digital Twins from ISAC Measurements
Wireless digital twins (DTs) rely on 3D environment models to predict radio propagation and support wireless-network decisions, yet these models are often initialized from imperfect 3D maps. Errors in building position, height, footprint, and orientation can therefore cause a high-fidelity propagation engine to simulate the wrong physical environment. In this paper, we study how a deployed wireless network can repair an existing DT using its own radio frequency (RF) measurements. In particular, we introduce Twin Residual Alignment and Calibration Engine (TRACE), a physics-grounded learning-based self-calibration framework that treats twin maintenance as residual alignment between the phys...
| Authors | Saad Masrur, Saeed R. Khosravirad, Ismail Guvenc |
|---|---|
| Primary category | cs.AI |
| Published | 26 September 2026 |
| Last updated at source | 29 September 2026 |
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