Adaptive Structured Sparse Bayesian Learning for Near-Field Non-Stationary Channel Estimation in XL-MIMO Systems

TL;DR

This paper introduces an adaptive structured sparse Bayesian learning method for improved near-field channel estimation in XL-MIMO systems, addressing non-stationarity and spherical-wave effects.

Contribution

It proposes an iterative adaptive dictionary updating framework with hierarchical priors to enhance channel representation without increasing dictionary size.

Findings

Outperforms existing polar-domain dictionary methods in simulations.

Achieves accurate near-field channel estimation with low dictionary overhead.

Effectively models spatial non-stationarity and spherical-wave propagation.

Abstract

Extremely large-scale multiple-input multiple-output (XL-MIMO) is a key enabler for sixth-generation (6G) communications. However, near-field channel estimation is particularly challenging due to spherical-wave propagation and spatial non-stationarity. To tackle this challenge, we propose a structured sparse Bayesian learning framework with adaptive dictionary updating for near-field non-stationary channel estimation. Specifically, the proposed method iteratively updates the distance parameters within an adaptive dictionary, thereby enhancing the representation capability without increasing the dictionary size. Moreover, we develop a hierarchical prior model that jointly captures polar-domain sparsity and structured dependency, enabling efficient Bayesian inference. Simulation results demonstrate that the proposed approach outperforms existing polar-domain dictionary-based methods while achieving low dictionary overhead.