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.

翻译：无线数字孪生可通过精确的物理建模与信号传播仿真，提供站点特定的合成信道信息。这有助于降低信道状态信息（CSI）获取的开销，尤其适用于大规模MIMO系统。为获得高质量数字孪生信道，传统方法是提升数字孪生保真度，即通过更精确的环境、传播及硬件建模实现。然而，该方法计算成本高昂，难以适用于实时应用。本文提出一种新框架，其核心并非校准数字孪生模型本身，而是对DFT域信道信息进行校准，以缩小低保真度数字孪生与高保真度数字孪生（或真实场景）之间的差距。该框架使得系统能够利用低复杂度数字孪生实时生成信道信息，同时保证质量不降级。为验证所提方法的有效性，我们以基于码本的CSI反馈作为案例研究，其中优化后的合成信道信息被用于为每个用户识别最相关的DFT码字。仿真结果表明，所提出的数字孪生校准方法在实现高精度CSI获取的同时，显著降低了数字孪生的计算开销，为实现数字孪生辅助的无线系统奠定了基础。