Digital Twin Aided Channel Estimation: Zone-Specific Subspace Prediction and Calibration

TL;DR

This paper presents a digital twin-assisted framework for efficient, zone-specific channel estimation in large-scale MIMO systems, combining subspace prediction, clustering, and reinforcement learning to improve accuracy and reduce feedback overhead.

Contribution

It introduces a novel digital twin-based approach that leverages coarse subspace predictions and RL calibration to enhance channel estimation in high-dimensional wireless environments.

Findings

Near-optimal channel estimation performance achieved.

Reinforcement learning improves subspace calibration accuracy.

Framework reduces feedback overhead significantly.

Abstract

Effective channel estimation in sparse and high-dimensional environments is essential for next-generation wireless systems, particularly in large-scale MIMO deployments. This paper introduces a novel framework that leverages digital twins (DTs) as priors to enable efficient zone-specific subspace-based channel estimation (CE). Subspace-based CE significantly reduces feedback overhead by focusing on the dominant channel components, exploiting sparsity in the angular domain while preserving estimation accuracy. While DT channels may exhibit inaccuracies, their coarse-grained subspaces provide a powerful starting point, reducing the search space and accelerating convergence. The framework employs a two-step clustering process on the Grassmann manifold, combined with reinforcement learning (RL), to iteratively calibrate subspaces and align them with real-world counterparts. Simulations show that digital twins not only enable near-optimal performance but also enhance the accuracy of subspace calibration through RL, highlighting their potential as a step towards learnable digital twins.