Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/17203
Title: Vertical Handover Decision based on Hybrid Artificial Neural Networks in HetNets of 5G
Authors: Kunarak S.
Duangchan T.
Keywords: 5G mobile communication systems
Backpropagation
Channel estimation
Interoperability
Long Term Evolution (LTE)
MIMO systems
Quality of service
Radio broadcasting
Wi-Fi
Wimax
Wireless local area networks (WLAN)
Decision-based
Hand over
Handover decision
Handover process
Hetnets
Hybrid artificial neural network
Hybrid neural networks
Ubiquitous networks
Vertical handovers
Wireless communications
Heterogeneous networks
Issue Date: 2021
Abstract: The 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.
URI: https://ir.swu.ac.th/jspui/handle/123456789/17203
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117487656&doi=10.1109%2fTENSYMP52854.2021.9550952&partnerID=40&md5=498c7acfce050f53e9e11c41c944304e
Appears in Collections:Scopus 1983-2021

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