The Application of Support Vector Machines with Gaussian Kernels for Overcoming Co-channel Interference

Félix Albu Dominique Martinez 1
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : This paper investigates the application of Support Vector machines (SVMs) for the equalization of communication systems corrupted with additive white Gaussian noise, intersymbol and co-channel interference. Performance obtained with SVMs for this task is compared to the one obtained with linear and Radial Basis Function (RBF) equalizers. The centers and the weights of the RBF networks are determined by the k-means and LMS algorithms, respectively. Experimental results shown that the SVM equalizer outperforms both linear and RBF equalizers, particularly for small training set. In case of time-varying channels, it is envisaged that the length of the training sequence which needs to be periodically transmitted would be reduced by SVM equalizers.
Type de document :
Communication dans un congrès
IEEE Workshop on Neural Networks for Signal Processing IX, Aug 1999, Madison, Wisconsin, U.S.A, pp.49-57, 1999
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Soumis le : mardi 26 septembre 2006 - 08:38:56
Dernière modification le : jeudi 11 janvier 2018 - 06:19:48

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  • HAL Id : inria-00098830, version 1

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Félix Albu, Dominique Martinez. The Application of Support Vector Machines with Gaussian Kernels for Overcoming Co-channel Interference. IEEE Workshop on Neural Networks for Signal Processing IX, Aug 1999, Madison, Wisconsin, U.S.A, pp.49-57, 1999. 〈inria-00098830〉

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