Channel equalization based on two weights neural network

Abstract

In this paper, we discuss the application of a two weights neural network (TWNN) to the channel equalization problem. In particular, the purpose of the paper is to improve the previously developed TWNN equalizer with training using K-means and LMS methods; reducing the TWNN network size by considering a lesser number of TWNN kernels, and developing new techniques for determining channel order which is required to specify the structure of an TWNN equalizer. A linear regression model was used for estimating the channel order. The basic idea of reducing the network size is to select the centers, based on the channel lag. This work includes the comparison of the limits of mean square error (MSE) convergence of both a linear equalizer and an TWNN equalizer. mean square error. © Springer-Verlag Berlin Heidelberg 2005.