Blind Equalization Without Gain Identification

Sergio Verdú, Rodney A. Kennedy

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Blind equalization up to a constant gain of linear time-invariant channels is studied. Dropping the requirement of gain identification allows equalizer anchoring. This results in the elimination of a degree of freedom that causes ill-convergence of conventional blind equalizers, and affords the possibility of using simple update rules based on the stochastic approximation of output energy. Unlike conventional blind equalizers, truncations of the nonrecursive infinite-dimensional realizations of those equalizers inherit the convergence properties of their infinitely parametrized counterparts. A globally convergent blind recursive equalizer for channels without all-pass sections is obtained based on the exact equalization of the minimum-phase part of the channel and the identification of its nonminimum-phase zeros.

Original languageEnglish (US)
Pages (from-to)292-297
Number of pages6
JournalIEEE Transactions on Information Theory
Issue number1
StatePublished - Jan 1993

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


  • ARMA models
  • Blind equalization
  • adaptive filtering
  • deeonvolution


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