Proceedings of the International Workshop on Applications of Neural Networks to Telecommunications

Channel Equalization by Distribution Learning: The Least Relative Entropy Algorithm

M. Kemal Sönmez and Tülay Adali*

Department of Mathematics, University of Maryland College Park, MD [e-mail]*Department of Electrical Engineering, University of Maryland Baltimore, MD [e-mail]

Abstract

We formulate the adaptive channel equalization as a conditional probability distribution estimation problem. Conditional probability density function of the transmitted signal given the received signal is parametrized by a sigmoidal perceptron. In this setting, the natural choice for a cost function is the relative entropy (Kullback-Leibler discriminant) between the desired probability density function and the perceptron model. ...