Sparse Bayesian Learning-Based Channel Estimation for Fluid Antenna Systems

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.