Deep learning with game theory assisted vertical handover optimization in a heterogeneous network

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dc.authorid0000-0002-3325-4731en_US
dc.authorid0000-0001-9043-8708en_US
dc.authorid0000-0002-8961-9963en_US
dc.authorid0000-0003-2095-5376en_US
dc.contributor.authorKayıkçı, Şafak
dc.contributor.authorUnnisa, Nazeer
dc.contributor.authorDas, Anupam
dc.contributor.authorKanna, S. K. Rajesh
dc.contributor.authorMurthy, Mantripragada Yaswanth Bhanu
dc.contributor.authorPreetha, N. S. Ninu
dc.date.accessioned2023-07-31T11:37:14Z
dc.date.available2023-07-31T11:37:14Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractProblem: In next-generation networks, users can optimize or tune their preferences with a seamless transfer of diverse access methodologies for maximizing the Quality of Service (QoS) and cost savings. In these heterogeneous wireless environments, users are prepared with several multimode wireless devices for maximizing media services through several access networks. Such networks may vary regarding energy usage, available bandwidth, technology, coverage range, monetary cost, etc. In recent days, vertical handover has attained higher performance owing to the improvements in mobility models through adopting the Fourth Generation (4G) technologies. On the other hand, these improvements are restricted to some cases, so, it does not offer support for generic mobility. Consequently, diverse strategies were implemented by considering these mobility models. However, it suffers from improper network selection, late and too-early handovers, repeated handovers, high packet loss, etc.Aim: This paper tackles the problem of vertical handover problem in the heterogeneous network using deep learning with game theory.Methods: The proposed model develops a non-cooperative game approach, in which all base stations compete selfishly to transmit at higher power. The overall performance in terms of throughput, handover, energy consumption, and load balancing is attained by optimizing the transmission power by the game theory. For performing this model, the required data like path loss, SINR, data rate, load, etc are generated by the deep learning called Recurrent Neural Network (RNN).Results: From the simulation findings, the handoff probability of the recommended RNN+Game Theory is correspondingly secured at 6.9%, 22.6%, and 8.2% superior to TOPSIS, ABC-PSO, and game theory when taking the time like 5 secs for user velocity as 30 km/h.Conclusion: Results show that the proposed game theoretical approach with deep learning provides a throughput enhancement while reducing the power consumption, in addition, to minimizing the unnecessary handover and balancing the load between base stations.en_US
dc.identifier.citationKayikci, S., Unnisa, N., Das, A., Kanna, S. K. R., Murthy, M. Y. B., Preetha, N. S. N., & Brammya, G. (2022). Deep Learning with Game Theory Assisted Vertical Handover Optimization in a Heterogeneous Network. International Journal on Artificial Intelligence Tools.en_US
dc.identifier.doi10.1142/S0218213023500124
dc.identifier.endpage33en_US
dc.identifier.issn0218-2130
dc.identifier.issn1793-6349
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85163813062en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1142/S0218213023500124
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11405
dc.identifier.volume32en_US
dc.identifier.wosWOS:001021492400010en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKayıkçı, Şafak
dc.language.isoenen_US
dc.publisherWorld Scientific Publishing Co Pte Ltden_US
dc.relation.ispartofInternational Journal on Artificial Intelligence Toolsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectVertical Handover Optimizationen_US
dc.subjectHeterogeneous Networken_US
dc.subjectRecurrent Neural Networken_US
dc.subjectGame Theoryen_US
dc.subjectDeep Learningen_US
dc.subjectSelection Schemeen_US
dc.titleDeep learning with game theory assisted vertical handover optimization in a heterogeneous networken_US
dc.typeArticleen_US

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