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

TL;DR

This paper introduces a joint low-rank and sparse Bayesian algorithm for channel estimation in ultra-massive MIMO systems, leveraging spatial channel properties to improve accuracy and reduce complexity.

Contribution

It presents a novel LRSBE algorithm that combines Bayesian learning and gradient descent within EM, specifically tailored for non-stationary ultra-massive MIMO channels.

Findings

Significantly outperforms existing methods in estimation accuracy.

Reduces computational complexity compared to state-of-the-art.

Effective across various SNR conditions.

Abstract

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.