Work

Sparse Bayesian Learning-Based Channel Estimation for Fluid Antenna Systems

Year: 2024

Type: article

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.

Source: IEEE Wireless Communications Letters

DOI: 10.1109/lwc.2024.3500218

Authors: Bowen Xu, Yu Chen, Qimei Cui, Xiaofeng Tao, Kai-Kit Wong

Institutions: Beijing University of Posts and Telecommunications, University College London

Language: English

Topic: Advanced Wireless Communication Techniques

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China

Awards: G5273970605, G7134976169