Digital Twin-Assisted Measurement Design and Channel Statistics Prediction

TL;DR

This paper presents a hybrid wireless channel prediction method combining uncalibrated digital twins and Gaussian processes to improve accuracy and efficiency using limited measurements.

Contribution

It introduces a novel framework that fuses environmental geometry from open-source maps with Gaussian process predictions, reducing calibration costs and measurement requirements.

Findings

Reduces measurement overhead for channel prediction.

Improves prediction accuracy with geometric priors.

Enables resource-efficient measurement strategies.

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 principled measurement selection by identifying informative probing locations under resource constraints. Through this integration of imperfect DTs with statistical learning, the proposed method reduces measurement overhead, improves prediction accuracy, and establishes a practical approach for resource-efficient wireless channel prediction.