A framework for radio resource allocation and SDMA grouping in massive MIMO systems
Autor(a) principal: | |
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Data de Publicação: | 2021 |
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/67210 |
Resumo: | This work proposes a framework for multiuser massive Multiple Input Multiple Output (MIMO) systems which is composed of three parts - clustering , grouping , and scheduling - and aims at maximizing the total system data rate considering Quality of Service (QoS) constraints. We firstly use a clustering algorithm to create clusters of spatially correlated Mobile Stations (MSs). Secondly, in the grouping part, we select a set of Space-Division Multiple Access (SDMA) groups from each cluster. These groups are used as candidate groups to receive Scheduling Unit (SU) in the scheduling part. In order to compose a group, we employ a metric that takes into account the trade-off between the spatial channel correlation and channel gain of MSs. In this context, it is proposed a suboptimal solution to avoid the high complexity required by the optimal solution. Thirdly and finally, we use the candidate SDMA groups from the grouping part to solve the data rate maximization problem considering QoS requirements. The scheduling part can be solved by our proposed optimal solution based on Branch and Bound (BB). However, since it has high computational complexity, we propose a suboptimal scheduling algorithm that presents a reduced complexity. In the simulation results, we evaluate the performance of both optimal and suboptimal solutions, as well as an adaptation of the Joint Satisfaction Maximization (JSM) scheduler to a massive MIMO scenario. Although the suboptimal solution presents a performance loss compared to the optimal one, it is more suitable for practical settings as it is able to obtain a good performance-complexity trade-off. Furthermore, we show that the choice of a suitable trade-off between the spatial channel correlation and channel gain improves the system performance. Finally, for a low number of available SDMA groups, the suboptimal solution presents near optimal outage and a throughput loss of only 10% in comparison to the high-complexity optimal solution while it outperforms the JSM solution in terms of outage and system throughput. |
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Repositório Institucional da Universidade Federal do Ceará (UFC) |
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A framework for radio resource allocation and SDMA grouping in massive MIMO systemsMassive MIMOChannel hardeningSDMA groupingRadio resource allocationQuality of serviceThis work proposes a framework for multiuser massive Multiple Input Multiple Output (MIMO) systems which is composed of three parts - clustering , grouping , and scheduling - and aims at maximizing the total system data rate considering Quality of Service (QoS) constraints. We firstly use a clustering algorithm to create clusters of spatially correlated Mobile Stations (MSs). Secondly, in the grouping part, we select a set of Space-Division Multiple Access (SDMA) groups from each cluster. These groups are used as candidate groups to receive Scheduling Unit (SU) in the scheduling part. In order to compose a group, we employ a metric that takes into account the trade-off between the spatial channel correlation and channel gain of MSs. In this context, it is proposed a suboptimal solution to avoid the high complexity required by the optimal solution. Thirdly and finally, we use the candidate SDMA groups from the grouping part to solve the data rate maximization problem considering QoS requirements. The scheduling part can be solved by our proposed optimal solution based on Branch and Bound (BB). However, since it has high computational complexity, we propose a suboptimal scheduling algorithm that presents a reduced complexity. In the simulation results, we evaluate the performance of both optimal and suboptimal solutions, as well as an adaptation of the Joint Satisfaction Maximization (JSM) scheduler to a massive MIMO scenario. Although the suboptimal solution presents a performance loss compared to the optimal one, it is more suitable for practical settings as it is able to obtain a good performance-complexity trade-off. Furthermore, we show that the choice of a suitable trade-off between the spatial channel correlation and channel gain improves the system performance. Finally, for a low number of available SDMA groups, the suboptimal solution presents near optimal outage and a throughput loss of only 10% in comparison to the high-complexity optimal solution while it outperforms the JSM solution in terms of outage and system throughput.IEEE Acess2022-07-18T18:54:57Z2022-07-18T18:54:57Z2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfMACIEL, T. F. et al. A framework for radio resource allocation and SDMA grouping in massive MIMO systems. IEEE Acess, vol. 9, p. 61680-61696, 20212169-3536http://www.repositorio.ufc.br/handle/riufc/67210Mauricio, Weskley Vinicius FernandesAraújo, Daniel CostaMaciel, Tarcísio FerreiraLima, Francisco Rafael Marquesengreponame:Repositório Institucional da Universidade Federal do Ceará (UFC)instname:Universidade Federal do Ceará (UFC)instacron:UFCinfo:eu-repo/semantics/openAccess2022-07-21T12:21:15Zoai:repositorio.ufc.br:riufc/67210Repositório InstitucionalPUBhttp://www.repositorio.ufc.br/ri-oai/requestbu@ufc.br || repositorio@ufc.bropendoar:2024-09-11T18:41:58.082913Repositório Institucional da Universidade Federal do Ceará (UFC) - Universidade Federal do Ceará (UFC)false |
dc.title.none.fl_str_mv |
A framework for radio resource allocation and SDMA grouping in massive MIMO systems |
title |
A framework for radio resource allocation and SDMA grouping in massive MIMO systems |
spellingShingle |
A framework for radio resource allocation and SDMA grouping in massive MIMO systems Mauricio, Weskley Vinicius Fernandes Massive MIMO Channel hardening SDMA grouping Radio resource allocation Quality of service |
title_short |
A framework for radio resource allocation and SDMA grouping in massive MIMO systems |
title_full |
A framework for radio resource allocation and SDMA grouping in massive MIMO systems |
title_fullStr |
A framework for radio resource allocation and SDMA grouping in massive MIMO systems |
title_full_unstemmed |
A framework for radio resource allocation and SDMA grouping in massive MIMO systems |
title_sort |
A framework for radio resource allocation and SDMA grouping in massive MIMO systems |
author |
Mauricio, Weskley Vinicius Fernandes |
author_facet |
Mauricio, Weskley Vinicius Fernandes Araújo, Daniel Costa Maciel, Tarcísio Ferreira Lima, Francisco Rafael Marques |
author_role |
author |
author2 |
Araújo, Daniel Costa Maciel, Tarcísio Ferreira Lima, Francisco Rafael Marques |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Mauricio, Weskley Vinicius Fernandes Araújo, Daniel Costa Maciel, Tarcísio Ferreira Lima, Francisco Rafael Marques |
dc.subject.por.fl_str_mv |
Massive MIMO Channel hardening SDMA grouping Radio resource allocation Quality of service |
topic |
Massive MIMO Channel hardening SDMA grouping Radio resource allocation Quality of service |
description |
This work proposes a framework for multiuser massive Multiple Input Multiple Output (MIMO) systems which is composed of three parts - clustering , grouping , and scheduling - and aims at maximizing the total system data rate considering Quality of Service (QoS) constraints. We firstly use a clustering algorithm to create clusters of spatially correlated Mobile Stations (MSs). Secondly, in the grouping part, we select a set of Space-Division Multiple Access (SDMA) groups from each cluster. These groups are used as candidate groups to receive Scheduling Unit (SU) in the scheduling part. In order to compose a group, we employ a metric that takes into account the trade-off between the spatial channel correlation and channel gain of MSs. In this context, it is proposed a suboptimal solution to avoid the high complexity required by the optimal solution. Thirdly and finally, we use the candidate SDMA groups from the grouping part to solve the data rate maximization problem considering QoS requirements. The scheduling part can be solved by our proposed optimal solution based on Branch and Bound (BB). However, since it has high computational complexity, we propose a suboptimal scheduling algorithm that presents a reduced complexity. In the simulation results, we evaluate the performance of both optimal and suboptimal solutions, as well as an adaptation of the Joint Satisfaction Maximization (JSM) scheduler to a massive MIMO scenario. Although the suboptimal solution presents a performance loss compared to the optimal one, it is more suitable for practical settings as it is able to obtain a good performance-complexity trade-off. Furthermore, we show that the choice of a suitable trade-off between the spatial channel correlation and channel gain improves the system performance. Finally, for a low number of available SDMA groups, the suboptimal solution presents near optimal outage and a throughput loss of only 10% in comparison to the high-complexity optimal solution while it outperforms the JSM solution in terms of outage and system throughput. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 2022-07-18T18:54:57Z 2022-07-18T18:54:57Z |
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. et al. A framework for radio resource allocation and SDMA grouping in massive MIMO systems. IEEE Acess, vol. 9, p. 61680-61696, 2021 2169-3536 http://www.repositorio.ufc.br/handle/riufc/67210 |
identifier_str_mv |
MACIEL, T. F. et al. A framework for radio resource allocation and SDMA grouping in massive MIMO systems. IEEE Acess, vol. 9, p. 61680-61696, 2021 2169-3536 |
url |
http://www.repositorio.ufc.br/handle/riufc/67210 |
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 |
IEEE Acess |
publisher.none.fl_str_mv |
IEEE Acess |
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_ |
1813028911093645312 |