Accurate channel state information (CSI) prediction is crucial for next-generation multiple-input multiple-output (MIMO) communication systems. Classical prediction methods often become inefficient for high-dimensional and rapidly time-varying channels. To improve prediction efficiency, it is essential to exploit the inherent low-rank tensor structure of the MIMO channel. Motivated by this observation, we propose a dynamic mode decomposition (DMD)-based prediction framework operating on the low-dimensional core tensors obtained via a Tucker decomposition. The proposed method predicts reduced-order channel cores, significantly lowering computational complexity. Simulation results demonstrate that the proposed approach preserves the dominant channel dynamics and achieves high prediction accuracy.

翻译：精确的信道状态信息预测对下一代多输入多输出通信系统至关重要。对于高维且快速时变的信道，经典预测方法往往效率低下。为提高预测效率，必须利用MIMO信道固有的低秩张量结构。基于这一观察，我们提出了一种基于动态模态分解的预测框架，该框架在通过Tucker分解获得的低维核心张量上运行。所提方法预测降阶后的信道核心张量，显著降低了计算复杂度。仿真结果表明，该方法能保持信道的主导动态特性，并实现较高的预测精度。