A Handover Decision Optimization Method Based on Data-Driven MLP in 5G Ultra-Dense Small Cell HetNets

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dc.authoridCalhan, Ali/0000-0002-5798-3103
dc.authoridRiaz, Hamidullah/0000-0001-5275-9922;
dc.contributor.authorRiaz, Hamidullah
dc.contributor.authorOzturk, Sitki
dc.contributor.authorAldirmaz-Colak, Sultan
dc.contributor.authorCalhan, Ali
dc.date.accessioned2025-10-11T20:48:45Z
dc.date.available2025-10-11T20:48:45Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractDeploying Ultra-Dense Small Cells (UDSCs) in Heterogeneous Networks (HetNets) introduces advantages such as increased capacity and expanded coverage over conventional HetNets. However, these advantages come at the expense of some challenges during the Handover (HO) process. Radio Link Failure (RLF) and Unnecessary Handover (UHO) are severe among these challenges. To address these issues, accurate setting and optimization of Handover Control Parameters (HCPs), including Handover Margin (HOM) and Time-To-Trigger (TTT), are necessary. Inaccurate adjustment and optimization of HCPs in live networks may lead to underperformance. Thus, this paper proposes a method that optimizes the obtained dataset by developing an algorithm that adjusts HOM and TTT based on related metrics such as RLF and UHO. The optimized dataset is then applied to a Multi-Layer Perception (MLP) model within a developed HO decision algorithm to predict both HOM and TTT, considering user speed, Reference Signal Received Power (RSRP), Signal to Interference plus Noise Ratio (SINR), and cell load. Simulation results showed that the proposed method outperforms the well-known A3 method in terms of Handover Rate (HOR), Handover Failure (HOF), Handover Ping-Pong (HOPP) and RLF by approximately 90.9%, 76.6%, 79.8% and 75%, respectively.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkiye (TUBITAK)en_US
dc.description.sponsorshipOpen access funding provided by the Scientific and Technological Research Council of Turkiye (TUBITAK). The authors did not receive support from any organization for the submitted work.en_US
dc.identifier.doi10.1007/s10922-025-09903-6
dc.identifier.issn1064-7570
dc.identifier.issn1573-7705
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85218355663en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s10922-025-09903-6
dc.identifier.urihttps://hdl.handle.net/20.500.12684/22075
dc.identifier.volume33en_US
dc.identifier.wosWOS:001420255100001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Networkand Systems Managementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectHetNetsen_US
dc.subjectHandoveren_US
dc.subjectHCPsen_US
dc.subjectLTE-Aen_US
dc.subjectMachine learningen_US
dc.subjectUltra-dense small cell networksen_US
dc.subject5Gen_US
dc.titleA Handover Decision Optimization Method Based on Data-Driven MLP in 5G Ultra-Dense Small Cell HetNetsen_US
dc.typeArticleen_US

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