TRACE: Learning to Self-Calibrate Wireless Digital Twins from ISAC Measurements

2026-09-26

AI Summary

This study addresses the distortion of radio frequency (RF) propagation simulations caused by 3D map inaccuracies in wireless digital twins. To mitigate this issue, a physics-guided self-calibration framework is proposed that leverages RF measurements to automatically rectify environmental models. By integrating ray tracing, RF back-projection, multi-view correctors, and iterative re-rendering techniques, the framework predicts correction parameters through residual alignment without requiring absolute layouts or sensor ordering. Experimental results demonstrate that the proposed approach reduces localization root mean square error (RMSE) to 0.3 meters and wall position error to 7.8 centimeters. The method significantly outperforms existing baselines while supporting zero-shot generalization across diverse scenarios.

Abstract

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 physical world and the current DT. Using the same sensing configuration as the physical measurements, TRACE ray-traces the current DT, coherently backprojects the measured and simulated RF onto a common world grid, and extracts the same local region around each building's current DT position. A multi-view corrector then fuses evidence across sensing nodes and neighboring buildings to predict a gated six-parameter correction per building, without relying on absolute layout or sensor ordering, and supports iterative correction through re-rendering. On 5,400 held-out samples from unseen simulated scenes at 28 GHz, TRACE reduces 3D position RMSE from 2.202 m to 0.302 m and yaw RMSE from 4.978{\deg} to 0.894{\deg}, outperforming ViT and U-Net baselines under changes in layout, building count, sensing-node count, and SNR. On measured 28 GHz RF data from the NIST outdoor courtyard, a model trained only on synthetic RF reduces mean planar wall-position error from 1.00 m to 7.8 cm, without measured-data fine-tuning or geometric labels. These results show that the discrepancy between measured and twin-rendered RF can serve as a learning signal for repairing a wireless DT.

Problem

• Wireless Digital Twins
• Self-Calibration
• Radio Propagation
• ISAC Measurements
• 3D Environment Models

Innovation

• Wireless Digital Twin
• Self-Calibration
• ISAC
• Ray Tracing
• Residual Alignment