Wireless Digital Twin Calibration: Refining DFT-Domain Channel Information

2026-03-17

AI Summary

This work addresses the challenge of obtaining real-time channel state information (CSI) in massive MIMO systems, where high-fidelity wireless digital twin models incur prohibitive computational overhead. Instead of directly enhancing model fidelity, the proposed framework shifts the calibration target to discrete Fourier transform (DFT)-domain channel representations, enabling low-complexity alignment between synthesized and actual channels. By integrating a codebook-based feedback mechanism, the method leverages the calibrated channels to efficiently select optimal DFT codewords. Simulation results demonstrate that the approach significantly reduces computational complexity while substantially improving CSI accuracy, thereby offering a practical pathway for deploying digital twin-aided wireless systems.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Wireless digital twins can be leveraged to provide site-specific synthetic channel information through precise physical modeling and signal propagation simulations. This can help reduce the overhead of channel state information (CSI) acquisition, particularly needed for large-scale MIMO systems. For high-quality digital twin channels, the classical approach is to increase the digital twin fidelity via more accurate modeling of the environment, propagation, and hardware. This, however, comes with high computational cost, making it unsuitable for real-time applications. In this paper, we propose a new framework that, instead of calibrating the digital twin model itself, calibrates the DFT-domain channel information to reduce the gap between the low-fidelity digital twin and its high-fidelity counterpart or the real world. This allows systems to leverage a low-complexity digital twin for generating real-time channel information without compromising quality. To evaluate the effectiveness of the proposed approach, we adopt codebook-based CSI feedback as a case study, where refined synthetic channel information is used to identify the most relevant DFT codewords for each user. Simulation results demonstrate the effectiveness of the proposed digital twin calibration approach in achieving high CSI acquisition accuracy while reducing the computational overhead of the digital twin. This paves the way for realizing digital twin assisted wireless systems.

Problem

• Wireless Digital Twin
• Channel State Information
• DFT-Domain Calibration
• Low-Fidelity Modeling
• CSI Acquisition

Innovation

• Wireless Digital Twin
• DFT-domain Calibration
• Channel State Information
• Low-complexity Modeling
• CSI Feedback