Successive Bayesian Reconstructor for FAS Channel Estimation

Autor:	Zhang, Zijian, Zhu, Jieao, Dai, Linglong, Heath Jr, Robert W.
Rok vydání:	2024
Předmět:	Computer Science - Information Theory Electrical Engineering and Systems Science - Signal Processing Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by IEEE WCNC 2024. This paper proposes S-BAR as a general solution to estimate FAS channels. More insights can be found in the journal version of this paper: arXiv:2312.06551. arXiv admin note: substantial text overlap with arXiv:2312.06551
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.02688 Zobrazit plný text záznamu View this record from Arxiv