ML-Aided Dynamic BSR Periodicity Adjustment for Enhanced UL Scheduling in Cellular Systems

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dc.contributor.authorChukhno, Nadezhda V.cs
dc.contributor.authorSaafi, Salwacs
dc.contributor.authorAndreev, Sergeycs
dc.coverage.issueAprilcs
dc.coverage.volume6cs
dc.date.accessioned2025-05-27T08:56:04Z
dc.date.available2025-05-27T08:56:04Z
dc.date.issued2025-04-15cs
dc.description.abstractContemporary research has revealed a limitation in the Uplink (UL) Buffer Status Report (BSR) scheduling procedure - its reliance on outdated information. In addition, a significant limitation in current BSR implementations lies in their inflexibility. The 3rd Generation Partnership Project (3GPP) specifications constrain BSR periodicities to certain quantized values based on Quality of Service (QoS) requirements for various applications. For instance, applications demanding low latency may require very small BSR periodicities, resulting in substantial overhead due to frequent BSR reports. This may result in the wastage of network resources in case of a low BSR periodicity setting. Alternatively, a high BSR periodicity setting may lead packets to wait more at the user buffer and thus result in higher packet latencies. To address these limitations, we propose a framework that predicts time intervals between packet arrivals and subsequently adjusts the BSR periodicity according to the predicted traffic arrivals. The simulation results demonstrate that the proposed Machine Learning (ML)-aided BSR reporting provides flexibility in BSR periodicity adapted to the intensity of traffic arrival and converges to optimal periodicity depending on the mean traffic arrival rate.en
dc.formattextcs
dc.format.extent3513-3527cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationIEEE Open Journal of the Communications Society. 2025, vol. 6, issue April, p. 3513-3527.en
dc.identifier.doi10.1109/OJCOMS.2025.3561002cs
dc.identifier.issn2644-125Xcs
dc.identifier.orcid0000-0002-2786-9377cs
dc.identifier.other197707cs
dc.identifier.urihttps://hdl.handle.net/11012/251051
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartofIEEE Open Journal of the Communications Societycs
dc.relation.urihttps://ieeexplore.ieee.org/document/10965763cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2644-125X/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectBuffer status reporten
dc.subjectuplink schedulingen
dc.subjectcellular networksen
dc.subjectmachine learningen
dc.subjectuplink traffic predictionen
dc.subjectwireless communicationsen
dc.titleML-Aided Dynamic BSR Periodicity Adjustment for Enhanced UL Scheduling in Cellular Systemsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-197707en
sync.item.dbtypeVAVen
sync.item.insts2025.05.27 10:56:04en
sync.item.modts2025.05.27 10:33:31en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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