Mode-Tensorized Canonical Polyadic Decomposition for MIMO Channel Estimation

2026-05-18

AI Summary

This work addresses the challenge of insufficient channel estimation accuracy in MIMO systems under low signal-to-noise ratio (SNR) conditions by proposing a higher-order representation method based on modal tensorization. The approach decomposes the channel tensor into multiple virtual modes and integrates canonical polyadic decomposition with sparse structural modeling to enhance path separability and intrinsic denoising capability. Furthermore, it introduces a virtual factor analysis metric grounded in the plane-wave propagation model to enable accurate tensor rank estimation and adaptive selection of dominant components. Compared to existing tensor-based methods, the proposed scheme significantly improves channel estimation performance at low SNR, demonstrating the distinct advantage of synergistically combining modal tensorization with structural priors.

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.

Problem

• MIMO channel estimation
• Canonical Polyadic decomposition
• tensor factorization
• sparse channel
• low SNR

Innovation

• Mode-Tensorized CP Decomposition
• MIMO Channel Estimation
• Tensor Factorization
• Path Separability
• Canonical Rank Estimation