The Application of Support Vector Machines with Gaussian Kernels for Overcoming Co-channel Interference
Identifieur interne : 004A94 ( Hal/Curation ); précédent : 004A93; suivant : 004A95The Application of Support Vector Machines with Gaussian Kernels for Overcoming Co-channel Interference
Auteurs : Félix Albu ; Dominique Martinez [France]Source :
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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.
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scheme="mix" xml:lang="fr"><term>channel equalization</term><term>machine à vecteurs supports</term><term>support vector machines</term><term>égalisation de canaux de transmission</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">The Application of Support Vector Machines with Gaussian Kernels for Overcoming Co-channel Interference</title><author role="aut"><persName><forename type="first">Félix</forename><surname>Albu</surname></persName><email></email><idno type="halauthor">129971</idno><orgName ref="#struct-364555"></orgName></author><author role="aut"><persName><forename type="first">Dominique</forename><surname>Martinez</surname></persName><email></email><idno type="halauthor">129970</idno><orgName ref="#struct-441569"></orgName><affiliation ref="#struct-2348"></affiliation></author><editor role="depositor"><persName><forename>Publications</forename><surname>Loria</surname></persName><email>publications@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2006-09-26 08:38:56</date><date type="whenModified">2016-05-19 01:09:29</date><date type="whenReleased">2006-09-28 15:22:44</date><date type="whenProduced">1999-08</date></edition><respStmt><resp>contributor</resp><name key="108626"><persName><forename>Publications</forename><surname>Loria</surname></persName><email>publications@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00098830</idno><idno type="halUri">https://hal.inria.fr/inria-00098830</idno><idno type="halBibtex">albu:inria-00098830</idno><idno type="halRefHtml">IEEE Workshop on Neural Networks for Signal Processing IX, Aug 1999, Madison, Wisconsin, U.S.A, pp.49-57, 1999</idno><idno type="halRef">IEEE Workshop on Neural Networks for Signal Processing IX, Aug 1999, Madison, Wisconsin, U.S.A, pp.49-57, 1999</idno></publicationStmt><seriesStmt><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA-CSAI" p="LORIA">Systèmes complexes et intelligence artificielle</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno></seriesStmt><notesStmt><note type="commentary">Colloque avec actes et comité de lecture.</note><note type="audience" n="1">Not set</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">The Application of Support Vector Machines with Gaussian Kernels for Overcoming Co-channel Interference</title><author role="aut"><persName><forename type="first">Félix</forename><surname>Albu</surname></persName><idno type="halAuthorId">129971</idno><orgName ref="#struct-364555"></orgName></author><author role="aut"><persName><forename type="first">Dominique</forename><surname>Martinez</surname></persName><idno type="halAuthorId">129970</idno><orgName ref="#struct-441569"></orgName><affiliation ref="#struct-2348"></affiliation></author></analytic><monogr><idno type="localRef">99-R-236 || albu99d</idno><meeting><title>IEEE Workshop on Neural Networks for Signal Processing IX</title><date type="start">1999-08</date><settlement>Madison, Wisconsin, U.S.A</settlement></meeting><imprint><biblScope unit="pp">49-57</biblScope><date type="datePub">1999-08</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="fr">machine à vecteurs supports</term><term xml:lang="fr">channel equalization</term><term xml:lang="fr">support vector machines</term><term xml:lang="fr">égalisation de canaux de transmission</term></keywords><classCode scheme="halDomain" n="info.info-oh">Computer Science [cs]/Other [cs.OH]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">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.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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