Sparse Bayesian learning approach to adaptive beamforming assisted receivers

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3 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 languageEnglish
Pages (from-to)182-184
Number of pages3
JournalIEEE Communications Letters
Volume11
Issue number2
DOIs
Publication statusPublished - 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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