Sparsifying dictionary learning for beamspace channel representation and estimation in millimeter-wave massive MIMO

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dc.authorid0000-0003-3375-0310
dc.authorid0000-0001-9474-7372
dc.contributor.authorAygül, Mehmet Ali
dc.contributor.authorNazzal, Mahmoud
dc.contributor.authorArslan, Hüseyin
dc.date.accessioned2023-10-06T06:28:15Z
dc.date.available2023-10-06T06:28:15Z
dc.date.issued2023
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü
dc.description.abstractMillimeter-wave (mmWave) massive multiple-input-multiple-output (mMIMO) is reported as a key enabler in fifth-generation communication and beyond. It is customary to use a lens antenna array to transform a mmWave mMIMO channel into a beamspace where the channel exhibits sparsity. This beamspace transformation is equivalent to performing a Fourier transformation of the channel. Still, a Fourier transformation is not necessarily optimal for many reasons. For example, it can cause a power leakage problem. Accordingly, this paper proposes using a learned sparsifying dictionary as the transformation operator leading to another beamspace for channel representation. Since a dictionary is obtained by training over actual channel measurements in an end-to-end manner, this transformation is shown to yield two immediate advantages. First is enhancing channel sparsity, thereby leading to more efficient pilot reduction. Second is improving the channel representation quality, thus reducing the underlying power leakage phenomenon. Consequently, this allows for improved channel estimation and facilitates beam selection in mmWave mMIMO. In addition, a learned dictionary is used as the channel estimation operator for the same reasons. Extensive simulations under various operating scenarios and environments validate the added benefits of using learned dictionaries in improving the channel estimation quality and beam selectivity, thus improving spectral efficiency.
dc.description.sponsorshipVestel Elektronik Sanayi ve Ticaret Anonim Sirketien_US
dc.identifier.citationAygül, M. A., Nazzal, M. ve Arslan, H. (2023). Sparsifying dictionary learning for beamspace channel representation and estimation in millimeter-wave massive MIMO. IEEE Access, 11, 98436-98451. https://doi.org/10.1109/ACCESS.2023.3313736
dc.identifier.doi10.1109/ACCESS.2023.3313736
dc.identifier.endpage98451
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85171588910
dc.identifier.scopusqualityQ1
dc.identifier.startpage98436
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2023.3313736
dc.identifier.urihttps://hdl.handle.net/20.500.12511/11531
dc.identifier.volume11
dc.identifier.wos001068796400001en_US
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorNazzal, Mahmoud
dc.institutionauthorArslan, Hüseyin
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.relation.tubitakinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/116E078
dc.rightsAttribution-NonCommercial-NoDerivs 4.0 International*
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectBeamspace Channel
dc.subjectChannel Estimation
dc.subjectChannel Representation
dc.subjectDictionary Learning
dc.subjectLens Antenna Array
dc.subjectMassive MIMO
dc.subjectMillimeter-Wave
dc.titleSparsifying dictionary learning for beamspace channel representation and estimation in millimeter-wave massive MIMO
dc.typeArticle

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