Digital Twin-Assisted Measurement Design and Channel Statistics Prediction

2026-03-24

AI Summary

This work proposes a novel approach to wireless channel statistical prediction that integrates uncalibrated, open-source map-based digital twins with Gaussian processes. Existing methods either rely on dense measurements while neglecting environmental geometry or require costly calibration to leverage geometric information from digital twins. In contrast, the proposed method uniquely embeds geometric priors directly into the Gaussian process framework, enabling accurate, scene-wide channel statistics prediction from only a few real-world measurements. Furthermore, it employs Bayesian optimization to actively select optimal measurement locations, minimizing data acquisition effort. The approach significantly reduces measurement overhead while enhancing prediction accuracy, offering a practical and data-efficient channel modeling solution for resource-constrained wireless systems.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

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.

Problem

• Digital Twin
• Channel Prediction
• Gaussian Processes
• Measurement Design
• Radio Map

Innovation

• Digital Twin
• Gaussian Process
• Channel Statistics Prediction
• Geometry-Induced Prior
• Measurement Design