Neural Networks Based Equalizer for Signal Restoration in Digital Communication Channels

Abstract

One of the main obstacles to reliable communications is the inter symbol interference (ISI). An equalizer is required at the receiver to mitigate the effects of non-ideal channel characteristics and restore the transmitted signal. This paper presents the equalization of digital communication channels using artificial neural network structures. The performances of a nonlinear equalizer using multilayer perceptron (MLP) trained by the back propagation algorithm is compared with a conventional linear traversal equalizer (LTE). Simulation results show that the performances of the MLP based Equalizer surpass significantly the classical LTE in term of the restored signal, the steady state mean square error (MSE) achievable and the minimum bit error rate attainable. The consistency in performance is observed in minimum phase and non-minimum phase channels as well.