Dynamics of the LRE algorithm: a distribution learning approach to adaptive equalization

Tulay Adali, M. Kemal Sonmez, Kartik Patel

Research output: Contribution to journalConference article

5 Scopus citations

Abstract

A general formulation for the adaptive equalization by distribution learning is proposed. The least relative entropy (LRE) algorithms for binary data communications is developed and analyzed with respect to its statistical and dynamical properties. It is shown that LRE learning is consistent and asymptotically normal, and that the algorithm can always recover from convergence at the wrong extreme as opposed to the MSE based MLP's. Finally, this fact is demonstrated using simulation examples.

Original languageEnglish (US)
Pages (from-to)929-932
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
StatePublished - Jan 1 1995
Externally publishedYes
EventProceedings of the 1995 20th International Conference on Acoustics, Speech, and Signal Processing. Part 2 (of 5) - Detroit, MI, USA
Duration: May 9 1995May 12 1995

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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