Sparse Bayesian Learning-Based Channel Estimation for Fluid Antenna Systems
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.
Authors
- Kai‐Kit Wong (ORCID: https://orcid.org/0000-0001-7521-0078)
- Qimei Cui (ORCID: https://orcid.org/0000-0003-1720-220X)
- Xiaofeng Tao (ORCID: https://orcid.org/0000-0001-9518-1622)
- Yu Chen (ORCID: https://orcid.org/0000-0003-4642-0898)
- Bowen Xu (ORCID: https://orcid.org/0009-0000-7828-8161)
Institutions
- Beijing University of Posts and Telecommunications (CN)
- University College London (GB)
Publication Details
- Journal
- IEEE Wireless Communications Letters
- Published
- 2024-11-18
- DOI
- https://doi.org/10.1109/lwc.2024.3500218
- Citations
- 14
- Primary Topic
- Advanced Adaptive Filtering Techniques
- Type
- article
- Field-Weighted Citation Impact
- 5.25
Funders
- National Key Research and Development Program of China