Mostrar el registro sencillo del ítem
dc.contributor.author
González, Gustavo José
dc.contributor.author
Gregorio, Fernando Hugo
dc.contributor.author
Cousseau, Juan Edmundo
dc.contributor.author
Werner, Stefan
dc.contributor.author
Wichman, Risto
dc.date.available
2017-01-24T14:37:00Z
dc.date.issued
2013-01
dc.identifier.citation
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
dc.identifier.issn
0165-1684
dc.identifier.uri
http://hdl.handle.net/11336/11769
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
Cfo Estimators
dc.subject
Cyclostationarity
dc.subject
Data-Aided
dc.subject
Ofdm
dc.subject.classification
Telecomunicaciones
dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Data-aided CFO estimators based on the averaged cyclic autocorrelation
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2017-01-19T19:54:12Z
dc.journal.volume
93
dc.journal.number
1
dc.journal.pagination
217-229
dc.journal.pais
Países Bajos
dc.journal.ciudad
Ámsterdam
dc.description.fil
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
dc.description.fil
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
dc.description.fil
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
dc.description.fil
Fil: Werner, Stefan. Aalto University. School of Electrical Engineering; Finlandia
dc.description.fil
Fil: Wichman, Risto. Aalto University. School of Electrical Engineering; Finlandia
dc.journal.title
Signal Processing
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0165168412002629
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://dx.doi.org/10.1016/j.sigpro.2012.07.032
Archivos asociados