Sparse Bayesian Learning-Based Channel Estimation for Fluid Antenna Systems
Bowen Xu, Yu Chen, Qimei Cui, Xiaofeng Tao, Kai‐Kit Wong
Abstract
Fluid antenna system (FAS) has emerged to give comparable performance to conventional multiple-input multiple-output (MIMO) systems with fewer radio-frequency (RF) chains. The performance of FAS depends on the accuracy of the channel state information (CSI) estimation. In this letter, we develop a sparse Bayesian learning (SBL) algorithm and an improved SBL algorithm to estimate FAS’s CSI. Simulation results demonstrate that both our proposed algorithms achieve higher accuracy in channel estimation compared to existing algorithms.
Topics & Concepts
Computer scienceChannel (broadcasting)Bayesian probabilityArtificial intelligenceTelecommunicationsAdvanced Adaptive Filtering TechniquesSpeech and Audio ProcessingAdvanced Wireless Communication Techniques