Flexible 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：柔性天线系统（FAS）能够在其空间连续范围内自由重构天线位置。为保持最优天线位置，获取FAS的信道状态信息（CSI）至关重要。尽管已有若干技术被提出，但现有的大多数FAS信道估计器需要若干信道假设，例如慢变特性和角域稀疏性。当这些假设不成立时，模型失配可能导致不可预见的性能损失。本文提出逐次贝叶斯重构器（S-BAR）作为估计FAS信道的通用解决方案。与基于模型的估计器不同，所提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FAS信道建模为随机过程，通过基于核的采样与回归逐次消除其不确定性。通过这种方式，回归后随机过程的预测均值可视为FAS信道的最大后验（MAP）估计器。仿真结果表明，在模型失配与模型匹配两种场景下，所提S-BAR相较于现有方案均能实现更高的估计精度。