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

Kyuho Hwang, Sooyong Choi

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


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
Issue number6
Publication statusPublished - 2012 Jun

Bibliographical note

Funding Information:
This work was supported in part by the National Research Foundation of Korea Grant funded by the Korean Government (MEST) (No. 2011-0005729 ) and the Seoul R&BD Program ( WR080951 ).

All Science Journal Classification (ASJC) codes

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


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