SDMA grouping based on unsupervised learning for multi-user MIMO systems

Detalhes bibliográficos
Autor(a) principal: Costa Neto, Francisco Hugo
Data de Publicação: 2020
Outros Autores: Maciel, Tarcísio Ferreira
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da Universidade Federal do Ceará (UFC)
Texto Completo: http://www.repositorio.ufc.br/handle/riufc/70645
Resumo: In this study, we investigate a spatial division multiple access (SDMA) grouping scheme to maximize the total data rate of a multi-user multiple input multiple output (MU-MIMO) system. Initially, we partition the set of mobile stations (MSs) into subsets according to their spatial compatibility. We explore different clustering algorithms, comparing them in terms of computational complexity and capability to partition MSs properly. Since we consider a scenario with a massive arrange of antenna elements and that operates on the mmWave scenario, we employ a hybrid beamforming scheme and analyze its behavior in terms of the total data rate. The analog and digital precoders exploit the channel information obtained from clustering and scheduling, respectively. The simulation results indicate that a proper partition of MSs into clusters can take advantage of the spatial compatibility effectively and reduce the multi-user (MU) interference. The hierarchical clustering (HC) enhances the total data rate 25% compared with the baseline approach, while the density-based spatial clustering of applications with noise (DBSCAN) increases the total data rate 20%.
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spelling SDMA grouping based on unsupervised learning for multi-user MIMO systemsSDMA groupingMulti-User MIMOHybrid beamformingUnsupervised learningClusteringIn this study, we investigate a spatial division multiple access (SDMA) grouping scheme to maximize the total data rate of a multi-user multiple input multiple output (MU-MIMO) system. Initially, we partition the set of mobile stations (MSs) into subsets according to their spatial compatibility. We explore different clustering algorithms, comparing them in terms of computational complexity and capability to partition MSs properly. Since we consider a scenario with a massive arrange of antenna elements and that operates on the mmWave scenario, we employ a hybrid beamforming scheme and analyze its behavior in terms of the total data rate. The analog and digital precoders exploit the channel information obtained from clustering and scheduling, respectively. The simulation results indicate that a proper partition of MSs into clusters can take advantage of the spatial compatibility effectively and reduce the multi-user (MU) interference. The hierarchical clustering (HC) enhances the total data rate 25% compared with the baseline approach, while the density-based spatial clustering of applications with noise (DBSCAN) increases the total data rate 20%.Journal of Communication and Information Systems2023-02-09T11:32:38Z2023-02-09T11:32:38Z2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfMACIEL, T. F.; COSTA NETO, F. H. SDMA grouping based on unsupervised learning for multi-user MIMO systems. Journal of Communication and Information Systems, [s.l.], v. 35, n. 1, p. 124-132, 2020. DOI: https://doi.org/10.14209/jcis.2020.131980-6604http://www.repositorio.ufc.br/handle/riufc/70645Costa Neto, Francisco HugoMaciel, Tarcísio Ferreiraengreponame:Repositório Institucional da Universidade Federal do Ceará (UFC)instname:Universidade Federal do Ceará (UFC)instacron:UFCinfo:eu-repo/semantics/openAccess2023-02-10T13:35:37Zoai:repositorio.ufc.br:riufc/70645Repositório InstitucionalPUBhttp://www.repositorio.ufc.br/ri-oai/requestbu@ufc.br || repositorio@ufc.bropendoar:2024-09-11T18:48:07.448407Repositório Institucional da Universidade Federal do Ceará (UFC) - Universidade Federal do Ceará (UFC)false
dc.title.none.fl_str_mv SDMA grouping based on unsupervised learning for multi-user MIMO systems
title SDMA grouping based on unsupervised learning for multi-user MIMO systems
spellingShingle SDMA grouping based on unsupervised learning for multi-user MIMO systems
Costa Neto, Francisco Hugo
SDMA grouping
Multi-User MIMO
Hybrid beamforming
Unsupervised learning
Clustering
title_short SDMA grouping based on unsupervised learning for multi-user MIMO systems
title_full SDMA grouping based on unsupervised learning for multi-user MIMO systems
title_fullStr SDMA grouping based on unsupervised learning for multi-user MIMO systems
title_full_unstemmed SDMA grouping based on unsupervised learning for multi-user MIMO systems
title_sort SDMA grouping based on unsupervised learning for multi-user MIMO systems
author Costa Neto, Francisco Hugo
author_facet Costa Neto, Francisco Hugo
Maciel, Tarcísio Ferreira
author_role author
author2 Maciel, Tarcísio Ferreira
author2_role author
dc.contributor.author.fl_str_mv Costa Neto, Francisco Hugo
Maciel, Tarcísio Ferreira
dc.subject.por.fl_str_mv SDMA grouping
Multi-User MIMO
Hybrid beamforming
Unsupervised learning
Clustering
topic SDMA grouping
Multi-User MIMO
Hybrid beamforming
Unsupervised learning
Clustering
description In this study, we investigate a spatial division multiple access (SDMA) grouping scheme to maximize the total data rate of a multi-user multiple input multiple output (MU-MIMO) system. Initially, we partition the set of mobile stations (MSs) into subsets according to their spatial compatibility. We explore different clustering algorithms, comparing them in terms of computational complexity and capability to partition MSs properly. Since we consider a scenario with a massive arrange of antenna elements and that operates on the mmWave scenario, we employ a hybrid beamforming scheme and analyze its behavior in terms of the total data rate. The analog and digital precoders exploit the channel information obtained from clustering and scheduling, respectively. The simulation results indicate that a proper partition of MSs into clusters can take advantage of the spatial compatibility effectively and reduce the multi-user (MU) interference. The hierarchical clustering (HC) enhances the total data rate 25% compared with the baseline approach, while the density-based spatial clustering of applications with noise (DBSCAN) increases the total data rate 20%.
publishDate 2020
dc.date.none.fl_str_mv 2020
2023-02-09T11:32:38Z
2023-02-09T11:32:38Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv MACIEL, T. F.; COSTA NETO, F. H. SDMA grouping based on unsupervised learning for multi-user MIMO systems. Journal of Communication and Information Systems, [s.l.], v. 35, n. 1, p. 124-132, 2020. DOI: https://doi.org/10.14209/jcis.2020.13
1980-6604
http://www.repositorio.ufc.br/handle/riufc/70645
identifier_str_mv MACIEL, T. F.; COSTA NETO, F. H. SDMA grouping based on unsupervised learning for multi-user MIMO systems. Journal of Communication and Information Systems, [s.l.], v. 35, n. 1, p. 124-132, 2020. DOI: https://doi.org/10.14209/jcis.2020.13
1980-6604
url http://www.repositorio.ufc.br/handle/riufc/70645
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Journal of Communication and Information Systems
publisher.none.fl_str_mv Journal of Communication and Information Systems
dc.source.none.fl_str_mv reponame:Repositório Institucional da Universidade Federal do Ceará (UFC)
instname:Universidade Federal do Ceará (UFC)
instacron:UFC
instname_str Universidade Federal do Ceará (UFC)
instacron_str UFC
institution UFC
reponame_str Repositório Institucional da Universidade Federal do Ceará (UFC)
collection Repositório Institucional da Universidade Federal do Ceará (UFC)
repository.name.fl_str_mv Repositório Institucional da Universidade Federal do Ceará (UFC) - Universidade Federal do Ceará (UFC)
repository.mail.fl_str_mv bu@ufc.br || repositorio@ufc.br
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