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https://hdl.handle.net/11147/9899
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DC Field | Value | Language |
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dc.contributor.author | Yılmaz, Saadet Simay | - |
dc.contributor.author | Özbek, Berna | - |
dc.date.accessioned | 2021-01-24T18:29:02Z | - |
dc.date.available | 2021-01-24T18:29:02Z | - |
dc.date.issued | 2020 | - |
dc.identifier.isbn | 978-172815207-3 | - |
dc.identifier.issn | 1550-2252 | - |
dc.identifier.uri | https://doi.org/10.1109/VTC2020-Spring48590.2020.9129553 | - |
dc.identifier.uri | https://hdl.handle.net/11147/9899 | - |
dc.description.abstract | Massive 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.sponsorship | This 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.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.relation.ispartof | IEEE Vehicular Technology Conference | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | compressive sensing | en_US |
dc.subject | Massive MIMO | en_US |
dc.subject | Sparse channel | en_US |
dc.subject | User selection | en_US |
dc.title | Compressive Sensing Based Low Complexity User Selection for Massive Mimo Systems | en_US |
dc.type | Conference Object | en_US |
dc.institutionauthor | Yılmaz, Saadet Simay | - |
dc.institutionauthor | Özbek, Berna | - |
dc.department | İzmir Institute of Technology. Electrical and Electronics Engineering | en_US |
dc.identifier.volume | 2020-May | en_US |
dc.identifier.scopus | 2-s2.0-85088287434 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.identifier.doi | 10.1109/VTC2020-Spring48590.2020.9129553 | - |
dc.relation.doi | 10.1109/VTC2020-Spring48590.2020.9129553 | en_US |
dc.coverage.doi | 10.1109/VTC2020-Spring48590.2020.9129553 | en_US |
item.fulltext | With Fulltext | - |
item.openairetype | Conference Object | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
crisitem.author.dept | 03.05. Department of Electrical and Electronics Engineering | - |
crisitem.author.dept | 03.05. 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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File | Size | Format | |
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Compressive_Sensing.pdf | 132.39 kB | Adobe PDF | View/Open |
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