Exploring Potential of ML-aided Mobile Traffic Prediction for Energy-efficient Optimization of Network Resources Using Real World Dataset

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dc.contributor.authorKoláčková, Anetacs
dc.contributor.authorSevgican, Salihcs
dc.contributor.authorUlu, Muhammet Fatihcs
dc.contributor.authorSadreddin, Jalecs
dc.contributor.authorMašek, Pavelcs
dc.contributor.authorHošek, Jiřícs
dc.contributor.authorJeřábek, Jancs
dc.contributor.authorTugcu, Tunacs
dc.coverage.issue1cs
dc.coverage.volume12cs
dc.date.accessioned2025-02-03T14:42:45Z
dc.date.available2025-02-03T14:42:45Z
dc.date.issued2024-07-01cs
dc.description.abstractTo meet the extremely stringent but diverse requirements of Beyond Fifth-Generation (B5G) networks, traffic-aware adaptive utilization of network resources is becoming essential. To cope with that, a detailed traffic data analysis enables opportunities for mobile network operators to improve the Quality of Service (QoS) in the next-generation mobile communication systems. This paper presents a comprehensive analysis of the real world data collected from an operator’s 4G+ and 5G infrastructure during a seven-month campaign. Efficient Machine Learning (ML) based network traffic predictions are presented together with a statistical model to develop optimal resource allocation strategies by using the data gathered during the pandemic, an era when the data volume, as well as the bandwidth requirements and the end users’ expectations, were significantly elevated in terms of QoS, given the huge shift to the online world. Data analysis confirmed the assumption that there are traffic changes during the day and the whole week, which helped us to find new research directions regarding resource allocation optimization of next-generation mobile networks. Furthermore, we introduce the Predictive Energy Saver for Baseband Units (PESBiU) algorithm, which utilizes traffic prediction and power consumption analysis to manage the power states (sleep or active) of BBUs in a network. The PESBiU algorithm utilizes the results from ML predictions to effectively balance energy efficiency and network performance, demonstrating its potential for practical deployment in future mobile communication networks by transitioning BBUs to sleep mode during low-traffic periods, thereby achieving significant power savings.en
dc.formattextcs
dc.format.extent93606-93622cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationIEEE Access. 2024, vol. 12, issue 1, p. 93606-93622.en
dc.identifier.doi10.1109/ACCESS.2024.3421633cs
dc.identifier.issn2169-3536cs
dc.identifier.orcid0000-0001-7491-1462cs
dc.identifier.orcid0000-0003-2976-6547cs
dc.identifier.orcid0000-0002-8382-9185cs
dc.identifier.orcid0000-0001-9487-5024cs
dc.identifier.other188972cs
dc.identifier.researcheridT-6266-2017cs
dc.identifier.researcheridB-1780-2010cs
dc.identifier.researcheridE-3929-2018cs
dc.identifier.scopus56529395900cs
dc.identifier.scopus37031030200cs
dc.identifier.scopus23011945600cs
dc.identifier.urihttps://hdl.handle.net/11012/249918
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartofIEEE Accesscs
dc.relation.urihttps://ieeexplore.ieee.org/document/10579805cs
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2169-3536/cs
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/cs
dc.subjectBeyond 5G networksen
dc.subjectData analysisen
dc.subjectMachine learningen
dc.subjectMobile trafficen
dc.subjectResource allocationen
dc.subjectBBU Energy Savingen
dc.titleExploring Potential of ML-aided Mobile Traffic Prediction for Energy-efficient Optimization of Network Resources Using Real World Dataseten
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-188972en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:42:45en
sync.item.modts2025.01.31 13:32:12en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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