DMD Prediction of MIMO Channel Using Tucker Decomposition

2026-03-16

AI Summary

This work addresses the inefficiency of conventional channel state information prediction methods in high-dimensional, rapidly time-varying MIMO channels by proposing a novel approach that integrates Tucker tensor decomposition with Dynamic Mode Decomposition (DMD). Specifically, the high-dimensional channel tensor is first compressed into a low-dimensional core tensor via Tucker decomposition, leveraging the inherent low-rank structure of MIMO channels. Subsequently, DMD is applied in this reduced space to efficiently capture and predict the dominant dynamic modes. This strategy significantly reduces computational complexity while preserving prediction accuracy by retaining the essential spatiotemporal dynamics of the channel. Simulation results demonstrate that the proposed method achieves substantially improved prediction efficiency without compromising accuracy, outperforming existing techniques in both speed and fidelity under realistic fast-fading conditions.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Intelligent Robots: State Estimation
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• MIMO channel
• channel state information
• CSI prediction
• high-dimensional channels
• time-varying channels

Innovation

• Dynamic Mode Decomposition
• Tucker Decomposition
• MIMO Channel Prediction
• Low-rank Tensor
• Channel State Information