Blind equalization method based on sparse Bayesian learning

Hwang Kyuho, Choi Sooyong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

A novel adaptive blind equalization method based on sparse Bayesian learning (blind relevance vector machine (RVM) equalizer) is proposed. This paper incorporates a Godard or constant modulus algorithm (CMA)-like error function into a general Bayesian framework. This Bayesian frame work 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 stability, performance and complexity compared to the blind SVM equalizer in terms of inter-symbol interference and bit error rate.

Original languageEnglish
Title of host publication2008 IEEE 67th Vehicular Technology Conference-Spring, VTC
Pages658-662
Number of pages5
DOIs
Publication statusPublished - 2008
Event2008 IEEE 67th Vehicular Technology Conference-Spring, VTC - Marina Bay, Singapore
Duration: 2008 May 112008 May 14

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252

Other

Other2008 IEEE 67th Vehicular Technology Conference-Spring, VTC
CountrySingapore
CityMarina Bay
Period08/5/1108/5/14

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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