Blind equalization method based on sparse bayesian learning

Kyuho Hwang, Sooyong Choi

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

A novel adaptive blind equalization method based on sparse Bayesian learning (blind relevance vector machine (RVM) equalizer) is proposed. This letter incorporates a Godard or constant modulus algorithm (CMA)-like error function into a general Bayesian framework. This Bayesian framework can obtain sparse solutions to regression tasks utilizing models linear in the parameters. By exploiting a probabilistic Bayesian learning framework, the sparse Bayesian learning provides the accurate model for the blind equalization, which typically utilizes fewer basis functions than the equalizer based on the popular and state-of-the-art support vector machine (SVM)-blind SVM equalizer. Simulation results show that the proposed blind RVM equalizer provides improved performances in terms of complexity, stability and intersymbol interference (ISI) and bit error rate (BER) in a linear channel and a similar BER performance in a nonlinear channel compared to the blind SVM equalizer.

Original languageEnglish
Article number4797892
Pages (from-to)315-318
Number of pages4
JournalIEEE Signal Processing Letters
Volume16
Issue number4
DOIs
Publication statusPublished - 2009 Dec 1

Fingerprint

Blind Equalization
Blind equalization
Bayesian Learning
Equalizer
Equalizers
Relevance Vector Machine
Support vector machines
Support Vector Machine
Bit error rate
Error Rate
Intersymbol Interference
Task Model
Intersymbol interference
Error function
Basis Functions
Regression Model
Modulus
Framework
Simulation

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

Cite this

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Blind equalization method based on sparse bayesian learning. / Hwang, Kyuho; Choi, Sooyong.

In: IEEE Signal Processing Letters, Vol. 16, No. 4, 4797892, 01.12.2009, p. 315-318.

Research output: Contribution to journalArticle

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