Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/9899
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dc.contributor.authorYılmaz, Saadet Simay-
dc.contributor.authorÖzbek, Berna-
dc.date.accessioned2021-01-24T18:29:02Z-
dc.date.available2021-01-24T18:29:02Z-
dc.date.issued2020-
dc.identifier.isbn978-172815207-3-
dc.identifier.issn1550-2252-
dc.identifier.urihttps://doi.org/10.1109/VTC2020-Spring48590.2020.9129553-
dc.identifier.urihttps://hdl.handle.net/11147/9899-
dc.description.abstractMassive Multiple-input Multiple-output (MIMO) is widely considered as a key enabler of the next-generation networks. In these systems, user selection strategies are important to achieve spatial diversity and maximize spectral efficiency. In this paper, a user selection algorithm is proposed with the reconstruction of the sparse Massive MIMO channel using Compressive Sensing (CS) algorithm. The proposed algorithm eliminates the users based on the channel correlation by employing the CS algorithm which reduces the feedback overhead in the system. The simulation results show that the proposed algorithm outperforms the traditional user selection algorithms in terms of sum data rate and computational complexity. Moreover, the effects of the sparsity level and feedback measurement on the performance are examined. © 2020 IEEE.en_US
dc.description.sponsorshipThis work has been funded by the European Union Horizon 2020. RISE 2018 scheme (H2020-MSCA-RISE-2018) under the Marie Sk?odowska-Curie grant agreement No. 823903 (RECENT).en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.ispartofIEEE Vehicular Technology Conferenceen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectcompressive sensingen_US
dc.subjectMassive MIMOen_US
dc.subjectSparse channelen_US
dc.subjectUser selectionen_US
dc.titleCompressive Sensing Based Low Complexity User Selection for Massive Mimo Systemsen_US
dc.typeConference Objecten_US
dc.institutionauthorYılmaz, Saadet Simay-
dc.institutionauthorÖzbek, Berna-
dc.departmentİzmir Institute of Technology. Electrical and Electronics Engineeringen_US
dc.identifier.volume2020-Mayen_US
dc.identifier.scopus2-s2.0-85088287434en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1109/VTC2020-Spring48590.2020.9129553-
dc.relation.doi10.1109/VTC2020-Spring48590.2020.9129553en_US
dc.coverage.doi10.1109/VTC2020-Spring48590.2020.9129553en_US
item.fulltextWith Fulltext-
item.openairetypeConference Object-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
crisitem.author.dept03.05. Department of Electrical and Electronics Engineering-
crisitem.author.dept03.05. Department of Electrical and Electronics Engineering-
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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