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dc.contributor.authorGonzález-Coma, José P.
dc.contributor.authorSuárez-Casal, Pedro
dc.contributor.authorCastro-Castro, Paula-María
dc.contributor.authorCastedo, Luis
dc.contributor.authorJoham, Michael
dc.date.accessioned2023-12-11T16:00:07Z
dc.date.available2023-12-11T16:00:07Z
dc.date.issued2018-11
dc.identifier.citationJ. P. Gonzalez-Coma, P. Suarez-Casal, P. M. Castro, L. Castedo and M. Joham, "CHANNEL COVARIANCE IDENTIFICATION IN FDD MASSIVE MIMO SYSTEMS," 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, USA, 2018, pp. 171-175, doi: 10.1109/GlobalSIP.2018.8646448.es_ES
dc.identifier.isbn978-1-7281-1295-4
dc.identifier.urihttp://hdl.handle.net/2183/34443
dc.description© 2018 IEEE. This version of the paper has been accepted for publication. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The final published paper is available online at: https://doi.org/10.1109/GlobalSIP.2018.8646448es_ES
dc.description.abstract[Abstract]: Many channel estimation methods in Massive MIMO FDD systems usually rely on the knowledge of the channel covariance matrix to operate. However, in real scenarios, this covariance is not known beforehand, and hence it should also be estimated. In this work, we investigate different existing techniques for covariance identification to achieve full knowledge of this matrix with very short training sequences. Moreover, we propose a modification of the spatial smoothing approach with the goal of improving the quality of the channel covariance identification.es_ES
dc.description.sponsorshipThis work has been funded by Xunta de Galicia (ED431C 2016-045, ED341D R2016/012, ED431G/01), AEI of Spain (TEC2015-69648-REDC, TEC2016-75067-C4-1-R), and ERDF funds (AEI/FEDER, EU).es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2016-045es_ES
dc.description.sponsorshipXunta de Galicia; ED341D R2016/012es_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01es_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TEC2015-69648-REDC/ES/RED COMONSENSes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TEC2016-75067-C4-1-R/ES/CODIFICACIÓN Y PROCESADO DE SEÑALES PARA REDES EMERGENTES DE COMUNICACIÓN Y DE SENSORES INALÁMBRICASes_ES
dc.relation.isversionofhttps://doi.org/10.1109/GlobalSIP.2018.8646448
dc.relation.urihttps://doi.org/10.1109/GlobalSIP.2018.8646448es_ES
dc.rightsTodos os dereitos reservados. All rights reserved.es_ES
dc.subjectCovariance identificationes_ES
dc.subjectMassive MIMOes_ES
dc.subjectFDDes_ES
dc.subjectMUSICes_ES
dc.titleChannel Covariance Identification in FDD Massive MIMO Systemses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.startPage171es_ES
UDC.endPage175es_ES
dc.identifier.doi10.1109/GlobalSIP.2018.8646448
UDC.conferenceTitle2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)es_ES


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