Neural networks applied to wireless communications

Autores
Stegmayer Machado, Georgina S.; Chiotti, Omar Juan Alfredo
Año de publicación
2006
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
This paper presents a time-delayed neural network (TDNN) model that has the capability of learning and predicting the dynamic behavior of nonlinear elements that compose a wireless communication system. This model could help speeding up system deployment by reducing modeling time. This paper presents results of effective application of the TDNN model to an amplifier, part of a wireless transmitter.
IFIP International Conference on Artificial Intelligence in Theory and Practice - Neural Nets
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Neural nets
Wireless communication
time-delayed neural network (TDNN)
nonlinear elements
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/23876

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spelling Neural networks applied to wireless communicationsStegmayer Machado, Georgina S.Chiotti, Omar Juan AlfredoCiencias InformáticasNeural netsWireless communicationtime-delayed neural network (TDNN)nonlinear elementsThis paper presents a time-delayed neural network (TDNN) model that has the capability of learning and predicting the dynamic behavior of nonlinear elements that compose a wireless communication system. This model could help speeding up system deployment by reducing modeling time. This paper presents results of effective application of the TDNN model to an amplifier, part of a wireless transmitter.IFIP International Conference on Artificial Intelligence in Theory and Practice - Neural NetsRed de Universidades con Carreras en Informática (RedUNCI)2006-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/23876enginfo:eu-repo/semantics/altIdentifier/isbn/0-387-34654-6info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-17T09:39:00Zoai:sedici.unlp.edu.ar:10915/23876Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-17 09:39:00.494SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Neural networks applied to wireless communications
title Neural networks applied to wireless communications
spellingShingle Neural networks applied to wireless communications
Stegmayer Machado, Georgina S.
Ciencias Informáticas
Neural nets
Wireless communication
time-delayed neural network (TDNN)
nonlinear elements
title_short Neural networks applied to wireless communications
title_full Neural networks applied to wireless communications
title_fullStr Neural networks applied to wireless communications
title_full_unstemmed Neural networks applied to wireless communications
title_sort Neural networks applied to wireless communications
dc.creator.none.fl_str_mv Stegmayer Machado, Georgina S.
Chiotti, Omar Juan Alfredo
author Stegmayer Machado, Georgina S.
author_facet Stegmayer Machado, Georgina S.
Chiotti, Omar Juan Alfredo
author_role author
author2 Chiotti, Omar Juan Alfredo
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Neural nets
Wireless communication
time-delayed neural network (TDNN)
nonlinear elements
topic Ciencias Informáticas
Neural nets
Wireless communication
time-delayed neural network (TDNN)
nonlinear elements
dc.description.none.fl_txt_mv This paper presents a time-delayed neural network (TDNN) model that has the capability of learning and predicting the dynamic behavior of nonlinear elements that compose a wireless communication system. This model could help speeding up system deployment by reducing modeling time. This paper presents results of effective application of the TDNN model to an amplifier, part of a wireless transmitter.
IFIP International Conference on Artificial Intelligence in Theory and Practice - Neural Nets
Red de Universidades con Carreras en Informática (RedUNCI)
description This paper presents a time-delayed neural network (TDNN) model that has the capability of learning and predicting the dynamic behavior of nonlinear elements that compose a wireless communication system. This model could help speeding up system deployment by reducing modeling time. This paper presents results of effective application of the TDNN model to an amplifier, part of a wireless transmitter.
publishDate 2006
dc.date.none.fl_str_mv 2006-08
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info:eu-repo/semantics/publishedVersion
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dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/0-387-34654-6
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
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