Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/17203
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dc.contributor.authorKunarak S.
dc.contributor.authorDuangchan T.
dc.date.accessioned2022-03-10T13:16:37Z-
dc.date.available2022-03-10T13:16:37Z-
dc.date.issued2021
dc.identifier.other2-s2.0-85117487656
dc.identifier.urihttps://ir.swu.ac.th/jspui/handle/123456789/17203-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85117487656&doi=10.1109%2fTENSYMP52854.2021.9550952&partnerID=40&md5=498c7acfce050f53e9e11c41c944304e
dc.description.abstractThe vertical handover process is the most significant in the heterogeneous wireless networks for fifth generation (5G). The main goal of 5G mobile communication systems is to keep the always best connected (ABC) in order to provide satisfying the Quality of Service (QoS). In this paper, the Wireless Fidelity (Wi-Fi) or Wireless Local Area Network (WLAN), Mobile Worldwide Interoperability for Microwave Access (Mobile WiMAX) and Long Term Evolution (LTE) are integrated as the ubiquitous wireless networks. Also, the authors propose the hybrid artificial neural networks (HANNs) that combines the back-propagation neural networks (BPNNs) and the radial basis function neural networks (RBFNNs). The HANNs help to decide when the handover could happen that is proper. To guarantee of the proposed method, the number of handover, the number of blocked call, the throughput data transmissions and the data latency are illustrated compared with the other two previous approaches, respectively. © 2021 IEEE.
dc.languageen
dc.subject5G mobile communication systems
dc.subjectBackpropagation
dc.subjectChannel estimation
dc.subjectInteroperability
dc.subjectLong Term Evolution (LTE)
dc.subjectMIMO systems
dc.subjectQuality of service
dc.subjectRadio broadcasting
dc.subjectWi-Fi
dc.subjectWimax
dc.subjectWireless local area networks (WLAN)
dc.subjectDecision-based
dc.subjectHand over
dc.subjectHandover decision
dc.subjectHandover process
dc.subjectHetnets
dc.subjectHybrid artificial neural network
dc.subjectHybrid neural networks
dc.subjectUbiquitous networks
dc.subjectVertical handovers
dc.subjectWireless communications
dc.subjectHeterogeneous networks
dc.titleVertical Handover Decision based on Hybrid Artificial Neural Networks in HetNets of 5G
dc.typeConference Paper
dc.rights.holderScopus
dc.identifier.bibliograpycitationTENSYMP 2021 - 2021 IEEE Region 10 Symposium. Vol , No. (2021)
dc.identifier.doi10.1109/TENSYMP52854.2021.9550952
Appears in Collections:Scopus 1983-2021

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