A Highly Efficient Channel Equalizer for Digital Communication System in Neural Network Paradigm

Abstract

This paper presents a new approach to equalization of communication channels using RBF Neural Networks as a classifier. Abundant research has been done in using Neural Network for the problem of channel equalization. The classical gradient based methods suffer from the problem of getting trapped in local minima. And the stochastic methods which can give a global optimum solution need long computational times. In this paper a novel method in which the task of an equalizer is decentralized by using a FIR filter for studying the channel characteristics and RBF Neural Network for classifying the received data. In the results it can be observed that this method of equalization provides optimum performance, which can be obtained using Tabu Search. Also, since we are using FIR filter, training will be very faster and LMS algorithm is computationally very simple.