Successive Bayesian Reconstructor for Channel Estimation in Fluid Antenna Systems

TL;DR

This paper introduces S-BAR, a Bayesian-based channel estimator for fluid antenna systems that adaptively improves estimation accuracy without relying on restrictive channel assumptions.

Contribution

The paper proposes a novel prior-aided Bayesian reconstructor for FAS channel estimation that outperforms existing methods, especially under model mismatch conditions.

Findings

S-BAR achieves higher accuracy than existing estimators.

It performs well in both model-matched and model-mismatched scenarios.

The method effectively eliminates uncertainty through kernel-based sampling.

Abstract

Fluid antenna systems (FASs) can reconfigure their antenna locations freely within a spatially continuous space. To keep favorable antenna positions, the channel state information (CSI) acquisition for FASs is essential. While some techniques have been proposed, most existing FAS channel estimators require several channel assumptions, such as slow variation and angular-domain sparsity. When these assumptions are not reasonable, the model mismatch may lead to unpredictable performance losses. In this paper, we propose the successive Bayesian reconstructor (S-BAR) as a general solution to estimate FAS channels. Unlike model-based estimators, the proposed S-BAR is prior-aided, which builds the experiential kernel for CSI acquisition. Inspired by Bayesian regression, the key idea of S-BAR is to model the FAS channels as a stochastic process, whose uncertainty can be successively eliminated by kernel-based sampling and regression. In this way, the predictive mean of the regressed stochastic process can be viewed as a Bayesian channel estimator. Simulation results verify that, in both model-mismatched and model-matched cases, the proposed S-BAR can achieve higher estimation accuracy than the existing schemes.