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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kunarak S. | |
dc.contributor.author | Duangchan T. | |
dc.date.accessioned | 2022-03-10T13:16:37Z | - |
dc.date.available | 2022-03-10T13:16:37Z | - |
dc.date.issued | 2021 | |
dc.identifier.other | 2-s2.0-85117487656 | |
dc.identifier.uri | https://ir.swu.ac.th/jspui/handle/123456789/17203 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117487656&doi=10.1109%2fTENSYMP52854.2021.9550952&partnerID=40&md5=498c7acfce050f53e9e11c41c944304e | |
dc.description.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. | |
dc.language | en | |
dc.subject | 5G mobile communication systems | |
dc.subject | Backpropagation | |
dc.subject | Channel estimation | |
dc.subject | Interoperability | |
dc.subject | Long Term Evolution (LTE) | |
dc.subject | MIMO systems | |
dc.subject | Quality of service | |
dc.subject | Radio broadcasting | |
dc.subject | Wi-Fi | |
dc.subject | Wimax | |
dc.subject | Wireless local area networks (WLAN) | |
dc.subject | Decision-based | |
dc.subject | Hand over | |
dc.subject | Handover decision | |
dc.subject | Handover process | |
dc.subject | Hetnets | |
dc.subject | Hybrid artificial neural network | |
dc.subject | Hybrid neural networks | |
dc.subject | Ubiquitous networks | |
dc.subject | Vertical handovers | |
dc.subject | Wireless communications | |
dc.subject | Heterogeneous networks | |
dc.title | Vertical Handover Decision based on Hybrid Artificial Neural Networks in HetNets of 5G | |
dc.type | Conference Paper | |
dc.rights.holder | Scopus | |
dc.identifier.bibliograpycitation | TENSYMP 2021 - 2021 IEEE Region 10 Symposium. Vol , No. (2021) | |
dc.identifier.doi | 10.1109/TENSYMP52854.2021.9550952 | |
Appears in Collections: | Scopus 1983-2021 |
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