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.

翻译：流体天线系统可在空间连续区域内自由重构其位置。为保持有利的天线位置，获取流体天线的信道状态信息至关重要。尽管已有一些技术被提出，但现有的大多数流体天线信道估计器需要若干信道假设，例如慢变特性和角度域稀疏性。当这些假设不成立时，模型失配可能导致不可预测的性能损失。本文提出连续贝叶斯重建器作为估计流体天线信道的通用解决方案。与基于模型的估计器不同，所提出的S-BAR采用先验辅助方法，为CSI获取构建经验核。受贝叶斯回归启发，S-BAR的核心思想是将流体天线信道建模为随机过程，其不确定性可通过基于核的采样与回归逐次消除。通过这种方式，回归后的随机过程预测均值可视为流体天线信道的最大后验估计器。仿真结果表明，在模型失配与模型匹配两种情况下，所提出的S-BAR均能比现有方案实现更高的估计精度。