Nonlinear Channel Equalization for Digital Communications Using DE-Trained Functional Link Artificial Neural Networks

Abstract

A major hindrance in the way of reliable and lossless communication is the inter symbol interference (ISI). To counter the effects of ISI and to have proper & reliable communication an adaptive equalizer can be employed at the receiver end. This paper considers the applications of artificial neural network structures (ANN) to the channel equalization problem. The problems related with channel nonlinearities and can be effectively subdued by application of ANNs. This paper contains a new approach to channel equalization using functional link artificial neural network (FLANN). In this paper we have incorporated the novel idea of utilizing an evolutionary technique called Differential Evolution (DE) for the training of FLANN we have compared the results with back propagation (BP) and Genetic Algorithm (GA) trained FLANNs. The comparison has been drawn based upon the minimum Mean Square Error (MSE) and Bit Error Rate (BER) performances. From this study it is evident that the DE trained FLANN performs better than the other types of equalizers.