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Data-Oriented Analysis of Uplink Transmission in Massive IoT System With Limited Channel Information

2024-06-28Artigo científico Acesso aberto
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dc.contributor.authorHamalainen, Jyri
dc.contributor.authorDinis, Rui
dc.contributor.authorIlter, Mehmet C.
dc.contributor.institutionFaculdade de Ciências e Tecnologia (FCT)
dc.contributor.pblInstitute of Electrical and Electronics Engineers (IEEE)
dc.date.accessioned2025-02-14T21:22:48Z
dc.date.available2025-02-14T21:22:48Z
dc.date.issued2024-06-28
dc.descriptionFunding informatiosn: This work was supported by Fundação para a Ciência e Tecnologia and Instituto de Telecomunicações under Project UIDB/50008/2020. Publisher Copyright: © 2020 IEEE.
dc.description.abstractRecently, the paradigm of massive ultra-reliable low-latency Internet of Things (IoT) communications (URLLC-IoT) has gained growing interest. Reliable delay-critical uplink transmission in vehicular IoT is a challenging task since low-complex devices typically do not support multiple antennas or demanding signal processing tasks. However, in many IoT services, the data volumes are small and deployments may include massive number of devices. For this kind of setup, we consider on a clustered uplink transmission with two cooperation approaches: First, we focus on scenario where location-based channel knowledge map (CKM) is applied to enable cooperation. Second, we consider a scenario where scarce channel side-information is applied inuplink transmission. In both scenarios we also model and analyse the impact of erroneous channel information. As being different from the existing literature, in the performance evaluation, we apply the recently introduced data-oriented approach in the context of short-packet transmissions over vehicular IoT networks. Specifically, it introduces a transient performance metric for small data transmissions the so-called delay outage rate (DOR), where the amount of data and available bandwidth play crucial roles. Results show that cooperation between clustered IoT devices may provide notable benefits in terms of increased range. It is noticed that the performance is heavily depending on the strength of the static channel component in the CKM-based cooperation. Also, it is shown that the channel side-information based cooperation is robust against changes in the radio environment but sensitive to possible errors in the channel side-information. Even with large IoT device clusters, side-information errors may set a limit for the use of services assuming high-reliability and low-latency where DOR is the relevant metric. The analytical derivations are validated through corresponding Monte Carlo numerical simulations, with only minor differences at low probability values.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent14
dc.format.extent1209510
dc.identifier.doi10.1109/OJVT.2024.3420224
dc.identifier.issn2644-1330
dc.identifier.otherPURE: 110415543
dc.identifier.otherPURE UUID: 8f990bdc-ef6d-4615-85df-96e9968b7677
dc.identifier.otherScopus: 85197034002
dc.identifier.otherORCID: /0000-0002-8520-7267/work/178129239
dc.identifier.urihttp://hdl.handle.net/10362/179082
dc.identifier.urlhttps://www.scopus.com/pages/publications/85197034002
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT
dc.relationInstituto de Telecomunicações
dc.subject6G
dc.subjectchannel knowledge map
dc.subjectchannel side-information
dc.subjectcoordinated transmission
dc.subjectdata-oriented approach
dc.subjecterroneous side-information
dc.subjectrice fading
dc.subjectAutomotive Engineering
dc.titleData-Oriented Analysis of Uplink Transmission in Massive IoT System With Limited Channel Informationen
dc.typejournal article
degois.publication.firstPage855
degois.publication.lastPage868
degois.publication.titleIEEE Open Journal of Vehicular Technology
degois.publication.volume5
dspace.entity.typePublication
oaire.awardNumberUIDB/50008/2020
oaire.awardTitleInstituto de Telecomunicações
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication6b1f1825-8ab4-43f1-b6be-46ce73bd063a
relation.isProjectOfPublication.latestForDiscovery6b1f1825-8ab4-43f1-b6be-46ce73bd063a

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