Geometry-Aided Channel Deduction with Partial Channel Estimates and Uncalibrated Digital Twin

2026-09-29

AI Summary

This study addresses the prohibitive pilot overhead and reliance on high-precision environmental modeling in acquiring high-dimensional channel state information (CSI) for wireless MIMO-OFDM systems. To overcome these limitations, this work proposes a low-overhead channel inference method leveraging uncalibrated digital twins. The approach innovatively introduces a random prompt enhancement mechanism that translates coarse geometric structures into CSI representations, thereby suppressing interference from unknown parameters and compensating for geometric inaccuracies. Furthermore, a deep learning architecture integrating digital twins with a channel inference network is constructed to support variable pilot patterns. Experimental results demonstrate that the proposed method significantly reduces pilot overhead while improving channel estimation accuracy and robustness, exhibiting superior generalization capabilities in previously unseen scenarios.

Abstract

The acquisition of high-dimensional channel state information (CSI) in wireless MIMO-OFDM communications usually requires high pilot overhead, or relies on accurate and complete positional or environmental information. In this paper, we propose a geometry-aided channel deduction (GCD) approach, which utilizes an uncalibrated digital twin (DT) with only approximate environmental geometry and positions to assist the channel acquisition. The key rationale behind is that, even imprecise geometric information, which can be easily obtained in advance through radio sensing technologies or existing geographic databases, provides certain structural features about the current channel; meanwhile, the coarse instantaneous channel estimates using only a small amount of pilots provide dedicated information that aligns with the channel structure and further compensates for the geometry inaccuracy and other channel unknowns. To this end, we first extract geometric features from the DT, which contain only simple structural information of the channel. Then we propose random prompt augmentation, a novel method to generate an appropriate prompt that converts geometric multi-path structure into a CSI-like representation while suppressing the disturbance of other unknown channel parameters. The prompt is then fused with the pilot-based instantaneous channel estimate via a channel deduction network. To further enhance the network's versatility, we incorporate pilot configurations into the existing learning architecture to support variable pilot patterns. Comprehensive experiments validate the superiority of the proposed method, which demonstrates high channel acquisition quality, low pilot overhead, and strong robustness. Furthermore, the structural prompt also serves as scenario-related context, enabling our approach to generalize well in new scenarios.

Problem

• Channel State Information
• MIMO-OFDM
• Pilot Overhead
• Digital Twin
• Channel Acquisition

Innovation

• Geometry-Aided Channel Deduction
• Uncalibrated Digital Twin
• Random Prompt Augmentation
• Channel Deduction Network
• Variable Pilot Patterns