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Fluid antenna systems (FASs) can reconfigure their antenna locations freelywithin a spatially continuous space. To keep favorable antenna positions, thechannel state information (CSI) acquisition for FASs is essential. While sometechniques have been proposed, most existing FAS channel estimators requireseveral channel assumptions, such as slow variation and angular-domainsparsity. When these assumptions are not reasonable, the model mismatch maylead to unpredictable performance loss. In this paper, we propose thesuccessive Bayesian reconstructor (S-BAR) as a general solution to estimate FASchannels. Unlike model-based estimators, the proposed S-BAR is prior-aided,which builds the experiential kernel for CSI acquisition. Inspired by Bayesianregression, the key idea of S-BAR is to model the FAS channels as a stochasticprocess, whose uncertainty can be successively eliminated by kernel-basedsampling and regression. In this way, the predictive mean of the regressedstochastic process can be viewed as the maximum a posterior (MAP) estimator ofFAS channels. Simulation results verify that, in both model-mismatched andmodel-matched cases, the proposed S-BAR can achieve higher estimation accuracythan the existing schemes.

Successive Bayesian Reconstructor for Channel Estimation in Fluid Antenna Systems