Blind equalizer for constant-modulus signals based on Gaussian process regression

Kyuho Hwang, Sooyong Choi

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

A new blind equalization method for constant modulus (CM) signals based on Gaussian process for regression (GPR) by incorporating a constant modulus algorithm (CMA)-like error function into the conventional GPR framework is proposed. The GPR framework formulates the posterior density function for weights using Bayes rule under the assumption of Gaussian prior for weights. The proposed blind GPR equalizer is based on linear-in-weights regression model, which has a form of nonlinear minimum mean-square error solution. Simulation results in linear and nonlinear channels are presented in comparison with the state-of-the-art support vector machine (SVM) and relevance vector machine (RVM) based blind equalizers. The simulation results show that the proposed blind GPR equalizer without cumbersome cross-validation procedures shows the similar performances to the blind SVM and RVM equalizers in terms of intersymbol interference and bit error rate.

Original languageEnglish
Pages (from-to)1397-1403
Number of pages7
JournalSignal Processing
Volume92
Issue number6
DOIs
Publication statusPublished - 2012 Jun 1

Fingerprint

Equalizers
Support vector machines
Blind equalization
Intersymbol interference
Mean square error
Bit error rate
Probability density function

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Cite this

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abstract = "A new blind equalization method for constant modulus (CM) signals based on Gaussian process for regression (GPR) by incorporating a constant modulus algorithm (CMA)-like error function into the conventional GPR framework is proposed. The GPR framework formulates the posterior density function for weights using Bayes rule under the assumption of Gaussian prior for weights. The proposed blind GPR equalizer is based on linear-in-weights regression model, which has a form of nonlinear minimum mean-square error solution. Simulation results in linear and nonlinear channels are presented in comparison with the state-of-the-art support vector machine (SVM) and relevance vector machine (RVM) based blind equalizers. The simulation results show that the proposed blind GPR equalizer without cumbersome cross-validation procedures shows the similar performances to the blind SVM and RVM equalizers in terms of intersymbol interference and bit error rate.",
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Blind equalizer for constant-modulus signals based on Gaussian process regression. / Hwang, Kyuho; Choi, Sooyong.

In: Signal Processing, Vol. 92, No. 6, 01.06.2012, p. 1397-1403.

Research output: Contribution to journalArticle

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