Successive Bayesian Reconstructor for FAS Channel Estimation

TL;DR

This work tackles the challenge of estimating high-dimensional FAS channels when traditional model-based estimators suffer from mismatches or overly restrictive assumptions. It introduces the Successive Bayesian Reconstructor (SBR), a two-stage, prior-aided Bayesian framework that models the port-channel vector ${\bf h}$ as a Gaussian process with a learnable kernel ${\boldsymbol \Sigma}$ and uses kernel-based sampling to progressively reduce uncertainty. The method combines offline design (selecting informative port measurements and computing reconstruction weights) with online regression, yielding a MAP estimate of ${\bf h}$ that outperforms existing schemes in both model-mismatched and model-matched scenarios. Empirical results on QuaDRiGa and SSC channel models show that SBR with experiential exponential or Bessel kernels can match or exceed the performance of a pre-trained covariance kernel, while requiring significantly fewer pilot measurements. This approach offers a practical, flexible pathway to reliable CSI for fluid antenna systems in realistic settings.

Abstract

Fluid antenna systems (FASs) can reconfigure their 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 loss. 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 the maximum a posterior (MAP) estimator of FAS channels. 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.

Figures (3)

• Figure 1: An illustration of employing sbr scheme to estimate fas channel $\bf h$. (a)-(d) provide the real part of $\bf h$ versus the index of ports. (e)-(h) provide the imaginary part of $\bf h$ versus the index of ports. Particularly, the curve "Truth" denotes the real channel $\bf h$, and the circle marks denote the sampled (measured) channels. The dotted line "Mean" denotes the posterior mean of Bayesian regression ${\bm \mu }_{\Omega}$, i.e., the estimated channel $\hat{\bf h}$. The highlighted shadows in the figures represent the confidence intervals of $\bf h$, defined as $[{\bm \mu }_{\Omega}(n)-3{{\bm \Sigma }_{\Omega}(n,n)},{\bm \mu }_{\Omega}(n)+3{{\bm \Sigma }_{\Omega}(n,n)}]$ for the $n$-th port.
• Figure 2: Model-mismatched case: The nmse as a function of the number of pilots $P$ under the assumption of QuaDRiGa channel model.
• Figure 3: Model-matched case: The nmse as a function of the number of pilots $P$ under the assumption of SSC channel model.