Channel Equalization Using Neural Networks: A Review

被引：134
作者：
Burse, Kavita [1 ]
Yadav, R. N. [1 ]
Shrivastava, S. C. [1 ]
机构：
[1] Maulana Azad Natl Inst Technol, Dept Elect & Commun Engn, Bhopal 462051, MP, India
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS | 2010年 / 40卷 / 03期
关键词：
Channel equalization; complex-valued neural networks (NNs); functional-link artificial NN (FLANN); multilayer perceptron (MLP); radial basis function (RBF); EXTREME LEARNING-MACHINE; SYSTEM-IDENTIFICATION; BAYESIAN EQUALIZER; PERCEPTRON; CLASSIFICATION;
D O I：
10.1109/TSMCC.2009.2038279
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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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收藏
页码：352 / 357
页数：6
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