Channel Equalization Using Neural Networks: A Review

Author

Burse, Kavita ; Yadav, Ram Narayan ; Shrivastava, S.C.

Author_Institution

Dept. of Electron. & Commun. Eng., Maulana Azad Nat. Inst. of Technol., Bhopal, India

Volume

40

Issue

3

fYear

2010

fDate

5/1/2010 12:00:00 AM

Firstpage

352

Lastpage

357

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.

Keywords

channel estimation; equalisers; intersymbol interference; multilayer perceptrons; radial basis function networks; recurrent neural nets; telecommunication computing; ANN; Feedback-based NN architectures; artificial neural networks; channel equalization; computational complexity; feedforward neural network; functional-link ANN; intersymbol interference; multilayer perceptron; neural networks; nonlinear channel models; radial basis function; receiver; recurrent NN equalizers; signal processing technique; training algorithms; Channel equalization; complex-valued neural networks (NNs); functional-link artificial NN (FLANN); multilayer perceptron (MLP); radial basis function (RBF);

fLanguage

English

Journal_Title

Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on

Publisher

ieee

ISSN

1094-6977

Type

jour

DOI

10.1109/TSMCC.2009.2038279

Filename

5406124