Data-aided CFO estimators based on the averaged cyclic autocorrelation

Autores
González, Gustavo José; Gregorio, Fernando Hugo; Cousseau, Juan Edmundo; Werner, Stefan; Wichman, Risto
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Wireless communication systems typically employ a repetitive preamble in each slot which is used for parameter acquisition. The repetitive preamble is useful for estimating the carrier frequency offset (CFO), usually based on the autocorrelation of the received signal. In this paper, we derive a family of novel data-aided CFO estimators. The proposed estimators are based on a new autocorrelation function which is defined using cyclostationary properties of the repetitive preamble. In contrast to previous approaches, the new estimators make use of high-order noise terms leading to an improved performance. We present a detailed analysis of the proposed estimators and provide closed-form expressions for the variance of the estimators. The new estimators are shown to outperform the existing estimators obtaining a moderate improvement at high signal to noise ratio (SNR) and a considerable improvement at low SNR, by means of a reasonable increase in computational complexity.
Fil: González, Gustavo José. Universidad Nacional del Sur. Departamento de Ingenieria Electrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación en Ingeniería Eléctrica; Argentina
Fil: Gregorio, Fernando Hugo. Universidad Nacional del Sur. Departamento de Ingenieria Electrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación en Ingeniería Eléctrica; Argentina
Fil: Cousseau, Juan Edmundo. Universidad Nacional del Sur. Departamento de Ingenieria Electrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación en Ingeniería Eléctrica; Argentina
Fil: Werner, Stefan. Aalto University. School of Electrical Engineering; Finlandia
Fil: Wichman, Risto. Aalto University. School of Electrical Engineering; Finlandia
Materia
Cfo Estimators
Cyclostationarity
Data-Aided
Ofdm
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/11769
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/11769
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Data-aided CFO estimators based on the averaged cyclic autocorrelationGonzález, Gustavo JoséGregorio, Fernando HugoCousseau, Juan EdmundoWerner, StefanWichman, RistoCfo EstimatorsCyclostationarityData-AidedOfdmhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Wireless communication systems typically employ a repetitive preamble in each slot which is used for parameter acquisition. The repetitive preamble is useful for estimating the carrier frequency offset (CFO), usually based on the autocorrelation of the received signal. In this paper, we derive a family of novel data-aided CFO estimators. The proposed estimators are based on a new autocorrelation function which is defined using cyclostationary properties of the repetitive preamble. In contrast to previous approaches, the new estimators make use of high-order noise terms leading to an improved performance. We present a detailed analysis of the proposed estimators and provide closed-form expressions for the variance of the estimators. The new estimators are shown to outperform the existing estimators obtaining a moderate improvement at high signal to noise ratio (SNR) and a considerable improvement at low SNR, by means of a reasonable increase in computational complexity.Fil: González, Gustavo José. Universidad Nacional del Sur. Departamento de Ingenieria Electrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación en Ingeniería Eléctrica; ArgentinaFil: Gregorio, Fernando Hugo. Universidad Nacional del Sur. Departamento de Ingenieria Electrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación en Ingeniería Eléctrica; ArgentinaFil: Cousseau, Juan Edmundo. Universidad Nacional del Sur. Departamento de Ingenieria Electrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación en Ingeniería Eléctrica; ArgentinaFil: Werner, Stefan. Aalto University. School of Electrical Engineering; FinlandiaFil: Wichman, Risto. Aalto University. School of Electrical Engineering; FinlandiaElsevier Science2013-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/11769González, Gustavo José; Gregorio, Fernando Hugo; Cousseau, Juan Edmundo; Werner, Stefan; Wichman, Risto; Data-aided CFO estimators based on the averaged cyclic autocorrelation; Elsevier Science; Signal Processing; 93; 1; 1-2013; 217-2290165-1684enginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0165168412002629info:eu-repo/semantics/altIdentifier/url/http://dx.doi.org/10.1016/j.sigpro.2012.07.032info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:54:48Zoai:ri.conicet.gov.ar:11336/11769instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:54:49.139CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Data-aided CFO estimators based on the averaged cyclic autocorrelation
title Data-aided CFO estimators based on the averaged cyclic autocorrelation
spellingShingle Data-aided CFO estimators based on the averaged cyclic autocorrelation
González, Gustavo José
Cfo Estimators
Cyclostationarity
Data-Aided
Ofdm
title_short Data-aided CFO estimators based on the averaged cyclic autocorrelation
title_full Data-aided CFO estimators based on the averaged cyclic autocorrelation
title_fullStr Data-aided CFO estimators based on the averaged cyclic autocorrelation
title_full_unstemmed Data-aided CFO estimators based on the averaged cyclic autocorrelation
title_sort Data-aided CFO estimators based on the averaged cyclic autocorrelation
dc.creator.none.fl_str_mv González, Gustavo José
Gregorio, Fernando Hugo
Cousseau, Juan Edmundo
Werner, Stefan
Wichman, Risto
author González, Gustavo José
author_facet González, Gustavo José
Gregorio, Fernando Hugo
Cousseau, Juan Edmundo
Werner, Stefan
Wichman, Risto
author_role author
author2 Gregorio, Fernando Hugo
Cousseau, Juan Edmundo
Werner, Stefan
Wichman, Risto
author2_role author
author
author
author
dc.subject.none.fl_str_mv Cfo Estimators
Cyclostationarity
Data-Aided
Ofdm
topic Cfo Estimators
Cyclostationarity
Data-Aided
Ofdm
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Wireless communication systems typically employ a repetitive preamble in each slot which is used for parameter acquisition. The repetitive preamble is useful for estimating the carrier frequency offset (CFO), usually based on the autocorrelation of the received signal. In this paper, we derive a family of novel data-aided CFO estimators. The proposed estimators are based on a new autocorrelation function which is defined using cyclostationary properties of the repetitive preamble. In contrast to previous approaches, the new estimators make use of high-order noise terms leading to an improved performance. We present a detailed analysis of the proposed estimators and provide closed-form expressions for the variance of the estimators. The new estimators are shown to outperform the existing estimators obtaining a moderate improvement at high signal to noise ratio (SNR) and a considerable improvement at low SNR, by means of a reasonable increase in computational complexity.
Fil: González, Gustavo José. Universidad Nacional del Sur. Departamento de Ingenieria Electrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación en Ingeniería Eléctrica; Argentina
Fil: Gregorio, Fernando Hugo. Universidad Nacional del Sur. Departamento de Ingenieria Electrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación en Ingeniería Eléctrica; Argentina
Fil: Cousseau, Juan Edmundo. Universidad Nacional del Sur. Departamento de Ingenieria Electrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación en Ingeniería Eléctrica; Argentina
Fil: Werner, Stefan. Aalto University. School of Electrical Engineering; Finlandia
Fil: Wichman, Risto. Aalto University. School of Electrical Engineering; Finlandia
description Wireless communication systems typically employ a repetitive preamble in each slot which is used for parameter acquisition. The repetitive preamble is useful for estimating the carrier frequency offset (CFO), usually based on the autocorrelation of the received signal. In this paper, we derive a family of novel data-aided CFO estimators. The proposed estimators are based on a new autocorrelation function which is defined using cyclostationary properties of the repetitive preamble. In contrast to previous approaches, the new estimators make use of high-order noise terms leading to an improved performance. We present a detailed analysis of the proposed estimators and provide closed-form expressions for the variance of the estimators. The new estimators are shown to outperform the existing estimators obtaining a moderate improvement at high signal to noise ratio (SNR) and a considerable improvement at low SNR, by means of a reasonable increase in computational complexity.
publishDate 2013
dc.date.none.fl_str_mv 2013-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/11769
González, Gustavo José; Gregorio, Fernando Hugo; Cousseau, Juan Edmundo; Werner, Stefan; Wichman, Risto; Data-aided CFO estimators based on the averaged cyclic autocorrelation; Elsevier Science; Signal Processing; 93; 1; 1-2013; 217-229
0165-1684
url http://hdl.handle.net/11336/11769
identifier_str_mv González, Gustavo José; Gregorio, Fernando Hugo; Cousseau, Juan Edmundo; Werner, Stefan; Wichman, Risto; Data-aided CFO estimators based on the averaged cyclic autocorrelation; Elsevier Science; Signal Processing; 93; 1; 1-2013; 217-229
0165-1684
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0165168412002629
info:eu-repo/semantics/altIdentifier/url/http://dx.doi.org/10.1016/j.sigpro.2012.07.032
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842269308440281088
score 13.13397

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