Fully complex multiplicative neural network model and its application to channel equalization

Abstract

In this paper a novel fully complex multiplicative neural network (MNN) algorithm is proposed to extract Quadrature Amplitude Modulation (QAM) signals when passed through a non linear channel in the presence of noise. The inputs, weights, activation functions and the output of the proposed MNN are complex valued. The training algorithm for the multilayer feed forward fully complex MNN is derived. The equalizer is tested on 4, 16 and 32 QAM signals and compared with split complex feed forward MNN equalizer. The proposed equalizer is implemented on nonlinear and nonminimum phase stationary channel. The fast converging algorithm gives lower bit error rate performance even in the presence of substantial noise. © 2010 Springer-Verlag Berlin Heidelberg.