Maximum partial likelihood framework for channel equalization by distribution learning

Tulay Adali, Xiao Liu, Ning Li, M. Kemal Sonmez

Research output: Contribution to conferencePaper

1 Scopus citations

Abstract

We present the general formulation for adaptive equalization by distribution learning in which conditional probability mass function (pmf) of the transmitted signal given the received is parametrized by a general neural network structure. The parameters of the pmf are computed by minimization of the accumulated relative entropy (ARE) cost function. The equivalence of ARE minimization to maximum partial log-likelihood (MPLL) estimation is established under certain regularity conditions which enables us to bypass the requirement that the true conditionals be known. The large sample properties of MPLL estimator are obtained under further regularity conditions, and the binary case with sigmoidal perceptron as the conditional pmf model is shown to be a special case of the new framework. Results are presented which show that the multilayer perceptron (MLP) equalizer based on ARE minimization can always recover from convergence at the wrong extreme whereas the mean square error (MSE) based MLP can not.

Original languageEnglish (US)
Pages541-550
Number of pages10
StatePublished - Jan 1 1995
Externally publishedYes
EventProceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95) - Cambridge, MA, USA
Duration: Aug 31 1995Sep 2 1995

Other

OtherProceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95)
CityCambridge, MA, USA
Period8/31/959/2/95

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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    Adali, T., Liu, X., Li, N., & Sonmez, M. K. (1995). Maximum partial likelihood framework for channel equalization by distribution learning. 541-550. Paper presented at Proceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95), Cambridge, MA, USA, .