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dc.contributor.authorNazzal, Mahmoud
dc.contributor.authorAygül, Mehmet Ali
dc.contributor.authorGörçin, Ali
dc.contributor.authorArslan, Hüseyin
dc.date.accessioned2020-01-02T11:27:00Z
dc.date.available2020-01-02T11:27:00Z
dc.date.issued2019en_US
dc.identifier.citationNazzal, M., Aygül, M. A., Görçin, A. ve Arslan, H. (2019). Dictionary learning-based beamspace channel estimation in millimeter-wave massive mimo systems with a lens antenna array. 15th IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC) içinde (20-25. ss.). Tangier, Morocco, June 24-28, 2019. http://doi.org/10.1109/IWCMC.2019.8766499en_US
dc.identifier.isbn9781538677476
dc.identifier.urihttp://doi.org/10.1109/IWCMC.2019.8766499
dc.identifier.urihttps://hdl.handle.net/20.500.12511/4863
dc.descriptionConference: 15th IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC) Location: Tangier, MOROCCO Date: JUN 24-28, 2019 Book Series: International Wireless Communications and Mobile Computing Conferenceen_US
dc.description.abstractRecent research considers the application of a lens antenna array in order to provide efficient beam selection in beamspace massive MIMO. Achieving the advantages of this beam selection paradigm requires efficient channel estimation in the beamspace. Along this line, beamspace sparsity is an efficient regularizer to this problem. In this paper, we propose using a dictionary trained over a set of example beam selection matrices, as a beam selection tool. In this context, a learned dictionary can more effectively guarantee the sparsity of the representation at the specified sparsity level, owing to the dictionary learning process. This means that it gives a better sparse representation, and, consequently, a better channel estimation quality. Simulations validate that using a trained dictionary improves the quality of channel estimation, as tested over two channel models with different operating scenarios.en_US
dc.description.sponsorshipIEEE; IEEE Morocco Section; Mohammed V University of Rabaten_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectBeamspace Channel Estimationen_US
dc.subjectBeam Selectionen_US
dc.subjectMillimeter-Wavesen_US
dc.subjectMassive MIMOen_US
dc.subjectSparse Codingen_US
dc.subjectDictionary Learninen_US
dc.titleDictionary learning-based beamspace channel estimation in millimeter-wave massive mimo systems with a lens antenna arrayen_US
dc.typeconferenceObjecten_US
dc.relation.ispartof15th IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC)en_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümüen_US
dc.authorid0000-0003-3375-0310en_US
dc.authorid0000-0001-9474-7372en_US
dc.identifier.startpage20en_US
dc.identifier.endpage25en_US
dc.relation.tubitakinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/116E078
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1109/IWCMC.2019.8766499en_US


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