Maximization of zeroor probability for adaptive channel equalization

Namyong Kim, Kyu Hwa Jeong, Liuqing Yang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

A new blind equalization algorithm that is based on maximizing the probability that the constant modulus errors concentrate near zero is proposed. The cost function of the proposed algorithm is to maximize the probability that the equalizer output power is equal to the constant modulus of the transmitted symbols. Two blind information-theoretic learning (ITL) algorithms based on constant modulus error signals are also introduced: One for minimizing the Euclidean probability density function distance and the other for minimizing the constant modulus error entropy. The relations between the algorithms and their characteristics are investigated, and their performance is compared and analyzed through simulations in multi-path channel environments. The proposed algorithm has a lower computational complexity and a faster convergence speed than the other ITL algorithms that are based on a constant modulus error. The error samples of the proposed blind algorithm exhibit more concentrated density functions and superior error rate performance in severe multi-path channel environments when compared with the other algorithms.

Original languageEnglish (US)
Pages (from-to)459-465
Number of pages7
JournalJournal of Communications and Networks
Volume12
Issue number5
DOIs
StatePublished - Oct 2010
Externally publishedYes

Keywords

  • Blind
  • Constant modulus error
  • Equalization
  • Information-theoretic learning (ITL)
  • Parzen window
  • Zeroor probability

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