Sparse Bayesian learning approach to adaptive beamforming assisted receivers

Sooyong Choi; Jong Moon Chung

doi:10.1109/LCOMM.2007.050231

Sparse Bayesian learning approach to adaptive beamforming assisted receivers

Sooyong Choi, Jong Moon Chung

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

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.

Original language	English
Pages (from-to)	182-184
Number of pages	3
Journal	IEEE Communications Letters
Volume	11
Issue number	2
DOIs	https://doi.org/10.1109/LCOMM.2007.050231
Publication status	Published - 2007 Feb

Bibliographical note

Funding Information:
Manuscript received February 18, 2005. The associate editor coordinating the review of this letter and approving it for publication was Prof. George K. Karagiannidis. This work was supported by Yonsei University Research Fund of 2005. The authors are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea (e-mail: {csyong, jmc}@yonsei.ac.kr). Digital Object Identifier 10.1109/LCOMM.2007.050231.

All Science Journal Classification (ASJC) codes

Modelling and Simulation
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/LCOMM.2007.050231

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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.",

author = "Sooyong Choi and Chung, {Jong Moon}",

note = "Funding Information: Manuscript received February 18, 2005. The associate editor coordinating the review of this letter and approving it for publication was Prof. George K. Karagiannidis. This work was supported by Yonsei University Research Fund of 2005. The authors are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea (e-mail: {csyong, jmc}@yonsei.ac.kr). Digital Object Identifier 10.1109/LCOMM.2007.050231.",

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Sparse Bayesian learning approach to adaptive beamforming assisted receivers

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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