SDMA grouping based on unsupervised learning for multi-user MIMO systems
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | |
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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Repositório Institucional da Universidade Federal do Ceará (UFC) |
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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 |
_version_ |
1813028951932534784 |