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Neural Computing and Applications

Erschienen in:

01.12.2014 | Original Article

Neural equalization applied to systems with bidimensional digital modulation

verfasst von: Marcelo A. C. Fernandes

Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2014

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Abstract

A new equalization model for digital communication systems is proposed, based on a multi-layer perceptron (MLP) artificial neural network with a backpropagation algorithm. Unlike earlier techniques, the proposed model, called the bidimensional neural equalizer, is composed of two independent MLP networks that operate in parallel for each dimension of the digital modulation scheme. A heuristic method to combine the errors of the two MLP networks is also proposed, with the aim of reducing the convergence time. Simulations performed for linear and nonlinear channels demonstrated that the new model could improve performance in terms of the bit error rate and the convergence time, compared to existing models.

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Titel: Neural equalization applied to systems with bidimensional digital modulation
verfasst von: Marcelo A. C. Fernandes
Publikationsdatum: 01.12.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 7-8/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1693-y

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