Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11598
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSun, H.-
dc.contributor.authorNg, C.-
dc.contributor.authorHuo, Y.-
dc.contributor.authorHu, R.Q.-
dc.contributor.authorWang, N.-
dc.contributor.authorChen, C.-M.-
dc.contributor.authorDemir, O.T.G.-
dc.date.accessioned2024-06-19T14:55:33Z-
dc.date.available2024-06-19T14:55:33Z-
dc.date.issued2023-
dc.identifier.isbn9798350324587-
dc.identifier.urihttps://doi.org/10.1109/FNWF58287.2023.10520592-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/11598-
dc.descriptionet al.;IEEE Antennas and Propagation Society (APS);IEEE Circuits and Systems Society (CAS);IEEE Communications Society (ComSoc);IEEE Electronics Packaging Society (EPS);IEEE Intelligent Transportation Systems Society (ITSS)en_US
dc.description6th IEEE Future Networks World Forum, FNWF 2023 -- 13 November 2023 through 15 November 2023 -- 199497en_US
dc.description.abstractThe use of a large number of antenna elements, known as Massive MIMO, is seen as a key enabling technology in the 5G and Beyond wireless ecosystem. The intelligent use of a multitude of antenna elements unleashes unprecedented flexibility and control on the physical channel of the wireless medium. Through Massive MIMO and other techniques, it is envisioned that the 5G and beyond wireless system will be able to support high throughput, high reliability (low bit-error-rate (BER)), high energy efficiency, low latency, and an internet-scale number of connected devices. Massive MIMO and related technologies will be deployed in the mid-band (sub 6 GHz) for coverage, all the way to mmWave bands to support large channel bandwidths. It is envisioned that Massive MIMO will be deployed in different environments: Frequency Division Duplex (FDD), (Time Division Duplex (TDD), indoor / outdoor, small cell, macro cell, and other heterogeneous networks (HetNet) configurations. Accurate and useful channel estimation remains a challenge in the efficient adoption of Massive MIMO techniques, and different performance-complexity tradeoffs may be supported by different Massive MIMO architectures such as digital, analog, and/or digital/analog hybrid. Carrier frequency offset (CFO), which arises due to the relative motion between the transmitter and receiver, is another important topic. Recently, maximum likelihood (ML) methods of CFO estimation have been proposed, that achieve very low root mean square (RMS) estimation errors, with a large scope for parallel processing and well suited for application with turbo codes. Massive MIMO opens up a whole new dimension of parameters where the wireless applications or other network layers may control or influence the operation and performance of the physical wireless channel. To fully reap the benefits of such flexibility, the latest advances in artificial intelligence (AI) and machine learning (ML) techniques will be leveraged to monitor and optimize the Massive MIMO sub-system. As such, a cross-layer open interface can facilitate exposing the programmability of Massive MIMO through techniques such as network slicing (NS) and network function virtualization (NFV). Finally, security needs to be integrated into the design of the system so the new functionality and performance of Massive MIMO can be utilized in a reliable manner. © 2023 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 2023 IEEE Future Networks World Forum: Future Networks: Imagining the Network of the Future, FNWF 2023en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject5Gen_US
dc.subjectaverage signal-to-noise ratio per biten_US
dc.subjectbeam optimizationen_US
dc.subjectbeamformingen_US
dc.subjectCFO estimationen_US
dc.subjectchannel estimationen_US
dc.subjectenergy efficiencyen_US
dc.subjectHetNeten_US
dc.subjecthybrid architectureen_US
dc.subjectMassive MIMOen_US
dc.subjectmmWaveen_US
dc.subject5G mobile communication systemsen_US
dc.subjectBeam forming networksen_US
dc.subjectBeamformingen_US
dc.subjectBit error rateen_US
dc.subjectChannel estimationen_US
dc.subjectEnergy efficiencyen_US
dc.subjectFrequency allocationen_US
dc.subjectHeterogeneous networksen_US
dc.subjectMaximum likelihood estimationen_US
dc.subjectMillimeter wavesen_US
dc.subjectMIMO systemsen_US
dc.subjectNetwork layersen_US
dc.subjectSpectrum efficiencyen_US
dc.subject5gen_US
dc.subjectAntenna elementen_US
dc.subjectAverage signal-to-noise ratio per biten_US
dc.subjectBeam optimizationen_US
dc.subjectCarrier frequency offset estimationen_US
dc.subjectEnabling technologiesen_US
dc.subjectHybrid architecturesen_US
dc.subjectMassive MIMOen_US
dc.subjectMm wavesen_US
dc.subjectPerformanceen_US
dc.subjectSignal to noise ratioen_US
dc.titleMassive Mimoen_US
dc.typeConference Objecten_US
dc.departmentTOBB ETÜen_US
dc.identifier.scopus2-s2.0-85194173919en_US
dc.institutionauthorDemir, O.T.G.-
dc.identifier.doi10.1109/FNWF58287.2023.10520592-
dc.authorscopusid57189043235-
dc.authorscopusid58144631700-
dc.authorscopusid56564766000-
dc.authorscopusid7202640793-
dc.authorscopusid55290251100-
dc.authorscopusid58146033600-
dc.authorscopusid57193644035-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Show simple item record



CORE Recommender

Page view(s)

42
checked on Dec 16, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.