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

2024-12-09Artigo científico Acesso aberto
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dc.contributor.authorBerra, Salah
dc.contributor.authorBenchabane, Abderrazak
dc.contributor.authorChakraborty, Sourav
dc.contributor.authorMaruta, Kazuki
dc.contributor.authorDinis, Rui
dc.contributor.authorBeko, Marko
dc.contributor.institutionFaculdade de Ciências e Tecnologia (FCT)
dc.contributor.pblInstitute of Electrical and Electronics Engineers (IEEE)
dc.date.accessioned2025-02-19T21:23:31Z
dc.date.available2025-02-19T21:23:31Z
dc.date.issued2024-12-09
dc.descriptionFunding 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.
dc.description.abstractMassive 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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent16
dc.format.extent3331757
dc.identifier.doi10.1109/OJVT.2024.3514749
dc.identifier.issn2644-1330
dc.identifier.otherPURE: 110801921
dc.identifier.otherPURE UUID: e57eea34-3e78-4dbf-9ba0-9ab22a002d36
dc.identifier.otherScopus: 85212063674
dc.identifier.otherORCID: /0000-0002-8520-7267/work/178488975
dc.identifier.urihttp://hdl.handle.net/10362/179398
dc.identifier.urlhttps://www.scopus.com/pages/publications/85212063674
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Base/UIDB%2F04111%2F2020/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT
dc.relationInstituto de Telecomunicações
dc.subjectChebychev acceleration
dc.subjectdeep unfolding
dc.subjectiterative method
dc.subjectlinear precoding
dc.subjectlow-complexity
dc.subjectMassive MIMO
dc.subjectnon-stationary
dc.subjectXL-MIMO
dc.subjectAutomotive Engineering
dc.titleA Low Complexity Linear Precoding Method for Extremely Large-Scale MIMO Systemsen
dc.typejournal article
degois.publication.firstPage240
degois.publication.lastPage255
degois.publication.titleIEEE Open Journal of Vehicular Technology
degois.publication.volume6
dspace.entity.typePublication
oaire.awardNumberUIDB/04111/2020
oaire.awardNumberUIDB/50008/2020
oaire.awardNumber2022.08786.PTDC
oaire.awardTitleInstituto de Telecomunicações
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Base/UIDB%2F04111%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/Concurso de Projetos de I&D em Todos os Domínios Científicos - 2022/2022.08786.PTDC/PT
oaire.fundingStreamConcurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) - Financiamento Base
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamConcurso de Projetos de I&D em Todos os Domínios Científicos - 2022
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication4b6f2b11-607d-42c8-b823-33462c22be80
relation.isProjectOfPublication6b1f1825-8ab4-43f1-b6be-46ce73bd063a
relation.isProjectOfPublication40f847e1-36b9-4565-8da4-bbfc596b54b8
relation.isProjectOfPublication.latestForDiscovery4b6f2b11-607d-42c8-b823-33462c22be80

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