Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework

2026-04-30

AI Summary

This work addresses the challenges of high measurement cost and privacy-security constraints in acquiring statistical channel state information for massive MIMO systems by proposing an efficient statistical channel fingerprint (sCF) construction method based on a unified tensor representation. The sCF reconstruction is formulated as a tensor recovery problem, and a novel LPWTNet architecture is developed that innovatively integrates Laplacian pyramid decomposition with a small-kernel convolution mechanism derived from wavelet transforms. A shared mask learning strategy is further introduced to simultaneously mitigate over-parameterization and significantly enhance the recovery of multi-scale high-frequency features. Experimental results demonstrate that the proposed method achieves substantially improved reconstruction accuracy and computational efficiency over existing approaches across diverse real-world scenarios.

Abstract

Channel fingerprint (CF) is considered a key enabler for facilitating the acquisition of channel state information (CSI) in massive multiple-input multiple-output (MIMO) communication systems. In this work, we investigate a novel type of CF that stores statistical CSI (sCSI) at each potential location, referred to as statistical CF (sCF). Specifically, we reveal the relationship between sCSI, namely the channel spatial covariance matrix (CSCM), and the channel power angular spectrum (CPAS). Building on this foundation, we construct a unified tensor representation of the sCF and further reduce its dimension by exploiting the eigenvalue decomposition of the CSCM and its correlation with the PAS. Considering the practical constraints imposed by measurement cost, privacy, and security, we focus on three representative scenarios and uniformly formulate them as tensor restoration tasks. To this end, we propose a unified tensor-based learning architecture, termed LPWTNet. The architecture incorporates a closed-form Laplacian pyramid (LP) decomposition and reconstruction framework that replaces the traditional encoder-decoder structure, enabling efficient inference while capturing multi-scale frequency subband characteristics of the sCF. Additionally, a shared mask learning strategy is introduced to adaptively refine high-frequency sCF components through level-wise adjustments. To achieve a larger receptive field without over-parameterization, we further propose a small-kernel convolution mechanism based on the wavelet transform (WT), which decouples convolution across different frequency components of the sCF and enhances feature extraction efficiency. Extensive experiments show that the proposed approach delivers competitive reconstruction accuracy and computational efficiency across various sCF construction scenarios when compared with state-of-the-art baselines.

Problem

• Statistical Channel Fingerprint
• Massive MIMO
• Channel State Information
• Tensor Restoration
• Spatial Covariance Matrix

Innovation

• tensor learning
• statistical channel fingerprint
• Laplacian pyramid
• wavelet transform
• massive MIMO