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.
@article{arxiv.2603.15468,
title = {DMD Prediction of MIMO Channel Using Tucker Decomposition},
author = {Irina Kopnina and Dmitry Artemasov and Sergey Matveev},
journal= {arXiv preprint arXiv:2603.15468},
year = {2026}
}
Comments
This work has been submitted to the IEEE for possible publication