Mode-Tensorized Canonical Polyadic Decomposition for MIMO Channel Estimation

TL;DR

This paper introduces a novel mode-tensorized CP decomposition method for MIMO channel estimation, leveraging tensor mode factorization to improve accuracy and robustness, especially in low SNR scenarios.

Contribution

The paper develops a mode-tensorized CP decomposition approach that enhances path separability and denoising in MIMO channel estimation, with a new metric for component analysis.

Findings

Improved channel estimation accuracy over conventional methods.

Enhanced component separation with increased tensor modes.

Better performance under low SNR conditions.

Abstract

This paper proposes a channel estimation method for Multiple-Input Multiple-Output (MIMO) systems based on Canonical Polyadic (CP) decomposition applied to a mode-factorized tensor representation of the channel. The proposed approach reshapes the original low-order channel tensor into a higher-order tensor by factorizing its modes into multiple virtual modes, thereby introducing additional dimensions. By exploiting the sparse structure of MIMO channels and the plane-wave propagation model in the far-field regime, the proposed mode tensorization enhances the separability of individual propagation paths. It is shown that increasing the number of tensor modes improves component separation and provides inherent denoising effects. Building on these properties, a mode-tensorized CP decomposition (MTCPD) algorithm is developed. In addition, a metric for analyzing the virtual factors obtained from MTCPD is proposed, enabling estimation of the canonical rank and selection of the most informative components contributing to overall system performance. Numerical results demonstrate that the proposed method improves channel estimation accuracy compared to conventional tensor-based approaches, particularly under low signal-to-noise ratio conditions.