Data detection in decentralized and distributed massive MIMO networks

Basit Öğe Kaydı
dc.authoridAlhabbash, Alaa/0000-0002-9296-8240
dc.authoridAlbreem, Mahmoud/0000-0002-6464-1101
dc.contributor.authorAlbreem, Mahmoud A.
dc.contributor.authorAlhabbash, Alaa
dc.contributor.authorAbu-Hudrouss, Ammar M.
dc.contributor.authorAlmohamad, Tarik Adnan
dc.date.accessioned2024-09-29T15:55:09Z
dc.date.available2024-09-29T15:55:09Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description.abstractIn order to meet the user demands in performance and quality of services (QoS) for beyond fifth generation (B5G) communication systems, research on decentralized and distributed massive multiple-input multiple output (M-MIMO) is initiated. Data detection techniques are playing a crucial role in realization and implementation of M-MIMO networks. Although most of detection techniques were proposed for centralized M-MIMO, there is a notable trend to propose efficient detection techniques for decentralized and distributed M-MIMO networks. This paper aims to provide insights on data detection techniques for decentralized and distributed M-MIMO to generalists of wireless communications. We garner the detection techniques for decentralized and distributed M-MIMO and present their performance, computational complexity, throughput, and latency so that a reader can find a distinction between different algorithms from a wider range of solutions. We present the detection techniques based on the following architectures: decentralized baseband processing (DBP), feedforward fully decentralized (FD), and feedforward partially decentralized (PD), FD based on coordinate descent (FD-CD), and FD based on recursive methods. In addition, the role of expectation propagation algorithm (EPA) in decentralized architectures is comprehensively reviewed. In each section, we also discuss the pros, cons, throughput, latency, performance, and complexity profile of each detector and related implementations. Moreover, the energy efficiency of several decentralized M-MIMO architectures is also illustrated. The cell-free M-MIMO (CF-M-MIMO) architecture is discussed with an overview of deployed detection schemes. This paper also illustrates the challenges and future research directions in decentralized and distributed M-MIMO networks.en_US
dc.description.sponsorshipResearch Council (TRC) of the Sultanate of Oman [TRC/BFP/ASU/01/2018]en_US
dc.description.sponsorshipAcknowledgment This research has been financially supported by the Research Council (TRC) of the Sultanate of Oman (agreement No. TRC/BFP/ASU/01/2018) .en_US
dc.identifier.doi10.1016/j.comcom.2022.03.015
dc.identifier.endpage99en_US
dc.identifier.issn0140-3664
dc.identifier.issn1873-703X
dc.identifier.scopus2-s2.0-85127278326en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage79en_US
dc.identifier.urihttps://doi.org/10.1016/j.comcom.2022.03.015
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4501
dc.identifier.volume189en_US
dc.identifier.wosWOS:000806644700008en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofComputer Communicationsen_US
dc.relation.publicationcategoryDiğeren_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject5Gen_US
dc.subjectMassive MIMOen_US
dc.subjectDecentralized MIMOen_US
dc.subjectCell-freeen_US
dc.subjectDetectionen_US
dc.subjectEstimationen_US
dc.titleData detection in decentralized and distributed massive MIMO networksen_US
dc.typeReviewen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu