DMD Prediction of MIMO Channel Using Tucker Decomposition

TL;DR

This paper introduces a novel DMD-based MIMO channel prediction method leveraging Tucker tensor decomposition to efficiently predict high-dimensional, rapidly changing channels with high accuracy.

Contribution

It proposes a new low-complexity prediction framework combining DMD and Tucker decomposition for MIMO channels, improving efficiency and accuracy.

Findings

Preserves dominant channel dynamics

Achieves high prediction accuracy

Reduces computational complexity

Abstract

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.