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

arXiv: https://arxiv.org/abs/2604.27574 · HTML: https://mlsanity.com/paper/2604.27574/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 51 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Builds on (most cited first, 30 of 31)

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- [Multi-level Wavelet-CNN for Image Restoration](https://mlsanity.com/paper/1805.07071/) (2018)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution](https://mlsanity.com/paper/1904.05049/) (2019)
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- [Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural Networks](https://mlsanity.com/paper/2308.10438/) (2023)
- [Integrated Sensing and Communication with Massive MIMO: A Unified Tensor Approach for Channel and Target Parameter Estimation](https://mlsanity.com/paper/2401.01738/) (2024)
- [Environment-Aware Hybrid Beamforming by Leveraging Channel Knowledge Map](https://mlsanity.com/paper/2206.08707/) (2022)
- [How Much Data is Needed for Channel Knowledge Map Construction?](https://mlsanity.com/paper/2312.06966/) (2023)
- [Radiomap Inpainting for Restricted Areas based on Propagation Priority and Depth Map](https://mlsanity.com/paper/2305.15526/) (2023)
- [An I2I Inpainting Approach for Efficient Channel Knowledge Map Construction](https://mlsanity.com/paper/2406.09822/) (2024)
- [Integrated Interpolation and Block-term Tensor Decomposition for Spectrum Map Construction](https://mlsanity.com/paper/2402.17138/) (2024)
- [Adaptive Bayesian Radio Tomography](https://mlsanity.com/paper/1804.02084/) (2018)
- [GDM4MMIMO: Generative Diffusion Models for Massive MIMO Communications](https://mlsanity.com/paper/2412.18281/) (2024)
- [Downlink Transmit Design for Massive MIMO LEO Satellite Communications](https://mlsanity.com/paper/2008.05343/) (2020)
- [Optimal Bilinear Equalizer for Cell-Free Massive MIMO Systems over Correlated Rician Channels](https://mlsanity.com/paper/2407.18531/) (2024)
- [Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach](https://mlsanity.com/paper/2505.07893/) (2025)

## Abstract (arXiv)

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.