Journal
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS
Volume 40, Issue 3, Pages 352-357Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCC.2009.2038279
Keywords
Channel equalization; complex-valued neural networks (NNs); functional-link artificial NN (FLANN); multilayer perceptron (MLP); radial basis function (RBF)
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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.
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