A channel equalizer using reduced decision feedback Chebyshev functional link artificial neural networks

Authors:
Wan-De Weng;Che-Shih Yang;Rui-Chang Lin
Affiliations:
Department of Electrical Engineering, National Yunlin University of Science and Technology, Taiwan;Department of Electrical Engineering, National Yunlin University of Science and Technology, Taiwan;Department of Electronic Engineering, Nankai Institute of Technology, Nantou 54216, Taiwan
Venue:
Information Sciences: an International Journal
Year:
2007

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Abstract

In this paper, a reduced decision feedback Chebyshev functional link artificial neural network (RDF-CFLANN) is proposed for the design of a nonlinear channel equalizer. An RDF-CFLANN structure uses functional expansion utilities to nonlinearly transform its input signals into the output space. In most MLP structures, one or more hidden layers are needed to nonlinearly map the input signals to the output signal space. Therefore, the complexity of the RDF-CFLANN structure is generally much lower than that of an MLP structure. In addition, the required amount of computing at the training mode can also be reduced. The comparisons of the mean squared error (MSE) and the average transmission bit error rate (BER) among RDF-CFLANN, DF-CFLANN and CFLANN are presented in this paper. Simulation results demonstrate that RDF-CFLANN presents the best performance among the three structures.