Neural Equalizer Performance Evaluation Using Genetic Algorithm

Artificial Neural Networks (ANN) have been successfully applied to deal with linear or nonlinear problems. The best ANN architecture choice is not a trivial task to be performed and requires some a priori knowledge. In this work, we propose a Genetic Algorithm (GA) evaluation approach to determine t...

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Bibliographic Details
Published inRevista IEEE América Latina Vol. 13; no. 10; pp. 3439 - 3446
Main Authors Andrade Mota, Tiago, Ferreira Leal, Jorgean, de Castro Lima, Antonio Cezar
Format Journal Article
LanguageEnglish
Published IEEE 01.10.2015
Subjects
Algorithms
Architecture
Artificial Neural Network
Artificial neural networks
Backpropagation
Bit error rate
Channels
Equalization
Equalizer
Equalizers
Genetic Algorithm
Genetic algorithms
Irrigation
Learning theory
Multipath Channel
Neural networks
Signal to noise ratio
Tasks
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Summary:Artificial Neural Networks (ANN) have been successfully applied to deal with linear or nonlinear problems. The best ANN architecture choice is not a trivial task to be performed and requires some a priori knowledge. In this work, we propose a Genetic Algorithm (GA) evaluation approach to determine the best combination of ANN and learning algorithm for equalization propose. A comparative analysis, using well known neural architectures, is presented in order to accomplish a 4-QAM equalization of signals submitted to Inter Symbol Interference (ISI), inherent in typical mobile communication channels. MLP, FLANN, PPN and three RNN based ANN structures, trained using backpropagation algorithm and others, have been evaluated.
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ISSN:1548-0992
1548-0992
DOI:10.1109/TLA.2015.7387252