Sparse Bayesian Learning Approach to Adaptive Beamforming Assisted Receivers

Author

Choi, Sooyong ; Chung, Jong-Moon

Author_Institution

Sch. of Electr. & Electron. Eng., Yonsei Univ., Seoul

Volume

11

Issue

2

fYear

2007

Firstpage

182

Lastpage

184

Abstract

In this letter, a new adaptive beamforming assisted receiver based on sparse Bayesian learning is proposed. We consider a general probabilistic Bayesian learning framework for obtaining sparse solutions to adaptive beamforming assisted receivers to improve the performance of an adaptive beamforming assisted receiver based on the minimum mean squared error (MMSE) scheme. Simulation experiments show that the bit error rate (BER) performance of the sparse Bayesian beamforming receiver shows an outstanding BER performance compared to MMSE beamforming receivers

Keywords

Bayes methods; array signal processing; error statistics; learning (artificial intelligence); least mean squares methods; probability; receivers; BER; MMSE scheme; adaptive beamforming assisted receiver; bit error rate; minimum mean squared error; probability; sparse Bayesian learning approach; Array signal processing; Bayesian methods; Bit error rate; Gaussian noise; Kernel; Machine learning; Sparse matrices; Support vector machine classification; Support vector machines; Wireless communication;

fLanguage

English

Journal_Title

Communications Letters, IEEE

Publisher

ieee

ISSN

1089-7798

Type

jour

DOI

10.1109/LCOMM.2007.050231

Filename

4115156