Channel equalization with perceptrons: An information-theoretic approach

Tülay Adah, M. Kemal Sönmez

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


We formulate the adaptive channel equalization as a conditional probability distribution learning problem. Conditional probability density function of the transmitted signal given the received signal is parametrized by a sigmoidal perceptron. In this framework, we use relative entropy (Kullback-Leibler distance) between the true and the estimated distributions as the cost function to be minimized. The true probabilities are approximated by their stochastic estimators resulting in a stochastic relative entropy cost function. This function is well-formed in the sense of Wittner and Denker, therefore gradient descent on this cost function is guaranteed to find a solution. The consistency and asymptotic normality of this learning scheme are shown via Maximum Partial Likelihood estimation of logistic models. As a practical example, we demonstrate that the resulting algorithm successfully equalizes multipath channels.

Original languageEnglish (US)
Article number390039
Pages (from-to)III297-III300
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - 1994
Externally publishedYes
EventProceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing. Part 2 (of 6) - Adelaide, Aust
Duration: Apr 19 1994Apr 22 1994

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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