[2603.23787] Digital Twin-Assisted Measurement Design and Channel Statistics Prediction

Computer Science > Information Theory arXiv:2603.23787 (cs) [Submitted on 24 Mar 2026] Title:Digital Twin-Assisted Measurement Design and Channel Statistics Prediction Authors:Robin J. Williams, Mahmoud Saad Abouamer, Petar Popovski View a PDF of the paper titled Digital Twin-Assisted Measurement Design and Channel Statistics Prediction, by Robin J. Williams and 1 other authors View PDF HTML (experimental) Abstract:Prediction of wireless channels and their statistics is a fundamental procedure for ensuring performance guarantees in wireless systems. Statistical radio maps powered by Gaussian processes (GPs) offer flexible, non-parametric frameworks, but their performance depends critically on the choice of mean and covariance functions. These are typically learned from dense measurements without exploiting environmental geometry. Digital twins (DTs) of wireless environments leverage computational power to incorporate geometric information; however, they require costly calibration to accurately capture material and propagation characteristics. This work introduces a hybrid channel prediction framework that leverages uncalibrated DTs derived from open-source maps to extract geometry-induced prior information for GP prediction. These structural priors are fused with a small number of channel measurements, enabling data-efficient prediction of channel statistics across the entire environment. By exploiting the uncertainty quantification inherent to GPs, the framework supports ...