A Low Complexity Linear Precoding Method for Extremely Large-Scale MIMO Systems

Detalhes bibliográficos
Autor(a) principal: Berra, Salah
Data de Publicação: 2024
Outros Autores: Benchabane, Abderrazak, Chakraborty, Sourav, Maruta, Kazuki, Dinis, Rui, Beko, Marko
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/179398
Resumo: Funding information: This work was supported in part by Fundação para a Ciência e Tecnologia (FCT) under the projects Copelabs UIDB/04111/2020 (https://doi.org/10.54499/UIDB/04111/2020), and in part by Instituto de Telecomunicações UIDB/50008/2020 (https://doi.org/10.54499/UIDB/50008/2020), in part by CELL-LESS6G 2022.08786.PTDC (https://doi.org/10.54499/2022.08786.PTDC), and in part by JST ASPIRE under Grant JPMJAP2325. Publisher Copyright: © 2020 IEEE.
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spelling A Low Complexity Linear Precoding Method for Extremely Large-Scale MIMO SystemsChebychev accelerationdeep unfoldingiterative methodlinear precodinglow-complexityMassive MIMOnon-stationaryXL-MIMOAutomotive EngineeringFunding information: This work was supported in part by Fundação para a Ciência e Tecnologia (FCT) under the projects Copelabs UIDB/04111/2020 (https://doi.org/10.54499/UIDB/04111/2020), and in part by Instituto de Telecomunicações UIDB/50008/2020 (https://doi.org/10.54499/UIDB/50008/2020), in part by CELL-LESS6G 2022.08786.PTDC (https://doi.org/10.54499/2022.08786.PTDC), and in part by JST ASPIRE under Grant JPMJAP2325. Publisher Copyright: © 2020 IEEE.Massive multiple-input multiple-output (MIMO) systems are critical technologies for the next generation of networks. In this field of research, new forms of deployment are emerging, such as extremely large-scale MIMO (XL-MIMO), in which the antenna array at the base station (BS) is of extreme dimensions. As a result, spatial non-stationary features emerge as users view just a section of the antenna array, known as the visibility regions (VRs). The XL-MIMO systems can achieve higher spectral efficiency, improve cell coverage, and provide significantly higher data rates than standard MIMO systems. It is a promising technology for future sixth-generation (6G) networks. However, due to the large number of antennas, linear precoding algorithms such as Zero-Forcing (ZF) and regularized Zero-Forcing (RZF) methods suffer from unacceptable computational complexity, primarily due to the required matrix inversion. This work aims to develop low-complexity precoding techniques for the downlink XL-MIMO system. These low-complexity linear precoding methods are based on Gauss-Seidel (GS) and Successive Over-Relaxation (SOR) techniques, which avoid calculating the complex matrix inversion and lead to stable linear precoding performance. To further enhance linear precoding performance, we incorporate the Chebyshev acceleration method with the SOR and GS methods, referred to as the Cheby-SOR and Cheby-GS methods. As these proposed methods require optimizing parameters, we create a deep unfolded network (DUN) to optimize the algorithm parameters. Our performance results demonstrate that the proposed method significantly reduces computational complexity from to O K2, where K represents the number of users. Moreover, our approach outperforms the original algorithms, requiring only a few iterations to achieve the RZF bit error rate (BER) performance.Faculdade de Ciências e Tecnologia (FCT)RUNBerra, SalahBenchabane, AbderrazakChakraborty, SouravMaruta, KazukiDinis, RuiBeko, Marko2025-02-19T21:23:31Z2024-12-092024-12-09T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article16application/pdfhttp://hdl.handle.net/10362/179398eng2644-1330PURE: 110801921https://doi.org/10.1109/OJVT.2024.3514749info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-02-24T01:50:48Zoai:run.unl.pt:10362/179398Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T20:40:17.392881Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv A Low Complexity Linear Precoding Method for Extremely Large-Scale MIMO Systems
title A Low Complexity Linear Precoding Method for Extremely Large-Scale MIMO Systems
spellingShingle A Low Complexity Linear Precoding Method for Extremely Large-Scale MIMO Systems
Berra, Salah
Chebychev acceleration
deep unfolding
iterative method
linear precoding
low-complexity
Massive MIMO
non-stationary
XL-MIMO
Automotive Engineering
title_short A Low Complexity Linear Precoding Method for Extremely Large-Scale MIMO Systems
title_full A Low Complexity Linear Precoding Method for Extremely Large-Scale MIMO Systems
title_fullStr A Low Complexity Linear Precoding Method for Extremely Large-Scale MIMO Systems
title_full_unstemmed A Low Complexity Linear Precoding Method for Extremely Large-Scale MIMO Systems
title_sort A Low Complexity Linear Precoding Method for Extremely Large-Scale MIMO Systems
author Berra, Salah
author_facet Berra, Salah
Benchabane, Abderrazak
Chakraborty, Sourav
Maruta, Kazuki
Dinis, Rui
Beko, Marko
author_role author
author2 Benchabane, Abderrazak
Chakraborty, Sourav
Maruta, Kazuki
Dinis, Rui
Beko, Marko
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Faculdade de Ciências e Tecnologia (FCT)
RUN
dc.contributor.author.fl_str_mv Berra, Salah
Benchabane, Abderrazak
Chakraborty, Sourav
Maruta, Kazuki
Dinis, Rui
Beko, Marko
dc.subject.por.fl_str_mv Chebychev acceleration
deep unfolding
iterative method
linear precoding
low-complexity
Massive MIMO
non-stationary
XL-MIMO
Automotive Engineering
topic Chebychev acceleration
deep unfolding
iterative method
linear precoding
low-complexity
Massive MIMO
non-stationary
XL-MIMO
Automotive Engineering
description Funding information: This work was supported in part by Fundação para a Ciência e Tecnologia (FCT) under the projects Copelabs UIDB/04111/2020 (https://doi.org/10.54499/UIDB/04111/2020), and in part by Instituto de Telecomunicações UIDB/50008/2020 (https://doi.org/10.54499/UIDB/50008/2020), in part by CELL-LESS6G 2022.08786.PTDC (https://doi.org/10.54499/2022.08786.PTDC), and in part by JST ASPIRE under Grant JPMJAP2325. Publisher Copyright: © 2020 IEEE.
publishDate 2024
dc.date.none.fl_str_mv 2024-12-09
2024-12-09T00:00:00Z
2025-02-19T21:23:31Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/179398
url http://hdl.handle.net/10362/179398
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2644-1330
PURE: 110801921
https://doi.org/10.1109/OJVT.2024.3514749
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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