Channel equalization using neural networks: a review

Authors:
Kavita Burse;R. N. Yadav;S. C. Shrivastava
Affiliations:
Department of Electronics and Communication Engineering, Maulana Azad National Institute of Technology, Bhopal, MP, India;Department of Electronics and Communication Engineering, Maulana Azad National Institute of Technology, Bhopal, MP, India;Department of Electronics and Communication Engineering, Maulana Azad National Institute of Technology, Bhopal, MP, India
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Year:
2010

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Abstract

Equalization refers to any signal processing technique used at the receiver to combat intersymbol interference in dispersive channels. This paper reviews the applications of artificial neural networks (ANNs) in modeling nonlinear phenomenon of channel equalization. The literature associated with different feedforward neural network (NN) based equalizers like multilayer perceptron, functional-link ANN, radial basis function, and its variants are reviewed. Feedback-based NN architectures like recurrent NN equalizers are described. Training algorithms are compared in terms of convergence time and computational complexity for nonlinear channel models. Finally, some limitation of current research activities and further research direction is provided.