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arXiv Open Access 2025

Joint Low-Rank and Sparse Bayesian Channel Estimation for Ultra-Massive MIMO Communications

Jianghan Ji Cheng-Xiang Wang Shuaifei Chen Chen Huang Xiping Wu +1 lainnya
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Abstrak

This letter investigates channel estimation for ultra-massive multiple-input multiple-output (MIMO) communications. We propose a joint low-rank and sparse Bayesian estimation (LRSBE) algorithm for spatial non-stationary ultra-massive channels by exploiting the low-rankness and sparsity in the beam domain. Specifically, the channel estimation integrates sparse Bayesian learning and soft-threshold gradient descent within the expectation-maximization framework. Simulation results show that the proposed algorithm significantly outperforms the state-of-the-art alternatives under different signal-to-noise ratio conditions in terms of estimation accuracy and overall complexity.

Topik & Kata Kunci

cs.IT eess.SP

Penulis (6)

J

Jianghan Ji

C

Cheng-Xiang Wang

S

Shuaifei Chen

C

Chen Huang

X

Xiping Wu

E

Emil Björnson

Format Sitasi

APA MLA BibTeX
Ji, J., Wang, C., Chen, S., Huang, C., Wu, X., Björnson, E. (2025). Joint Low-Rank and Sparse Bayesian Channel Estimation for Ultra-Massive MIMO Communications. https://arxiv.org/abs/2512.04470

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Informasi Jurnal
Tahun Terbit
2025
Bahasa
en
Sumber Database
arXiv
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Open Access ✓