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.

翻译：无线信道及其统计特性的预测是保障无线系统性能的基础流程。基于高斯过程（GP）的统计无线电地图提供了灵活的非参数化框架，但其性能在很大程度上取决于均值函数和协方差函数的选择。这些函数通常需通过密集测量进行学习，而未利用环境几何信息。无线环境的数字孪生（DT）借助计算能力融入几何信息，但需要昂贵的校准才能准确反映材料特性和传播特性。本文提出一种混合信道预测框架，利用基于开源地图的非校准DT提取几何诱导的先验信息，用于GP预测。这些结构先验与少量信道测量融合，能够在整个环境中实现数据高效的统计信道预测。通过利用GP固有的不确定性量化能力，该框架可在资源约束下识别信息丰富的探测位置，从而支持基于原理的测量选择。通过将非完美DT与统计学习相结合，所提方法降低了测量开销，提升了预测精度，并为资源高效的无线信道预测建立了实用方案。