Sparse Bayesian learning approach to adaptive beamforming assisted receivers

Research output: Contribution to journalArticle

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 1

Fingerprint

Bayesian Learning
Beamforming
Receiver
Mean Squared Error
Bit error rate
Error Rate
Simulation Experiment
Experiments

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

@article{12a816b67d884230b316962a144fd84e,
title = "Sparse Bayesian learning approach to adaptive beamforming assisted receivers",
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 Jong-Moon Chung",
year = "2007",
month = "2",
day = "1",
doi = "10.1109/LCOMM.2007.050231",
language = "English",
volume = "11",
pages = "182--184",
journal = "IEEE Communications Letters",
issn = "1089-7798",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "2",

}

Sparse Bayesian learning approach to adaptive beamforming assisted receivers. / Choi, Sooyong; Chung, Jong-Moon.

In: IEEE Communications Letters, Vol. 11, No. 2, 01.02.2007, p. 182-184.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Sparse Bayesian learning approach to adaptive beamforming assisted receivers

AU - Choi, Sooyong

AU - Chung, Jong-Moon

PY - 2007/2/1

Y1 - 2007/2/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=33947706204&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33947706204&partnerID=8YFLogxK

U2 - 10.1109/LCOMM.2007.050231

DO - 10.1109/LCOMM.2007.050231

M3 - Article

VL - 11

SP - 182

EP - 184

JO - IEEE Communications Letters

JF - IEEE Communications Letters

SN - 1089-7798

IS - 2

ER -