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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/138294


    Title: A Study on Handover Mechanism in 5G Non-standalone Network
    Authors: 張宏慶
    Chang, Kai-Sheng
    Jang, Hung-Chin
    Contributors: 資科系
    Date: 2020-12
    Issue Date: 2021-12-09 16:11:25 (UTC+8)
    Abstract: 3GPP divides the deployment of 5G networks into an initial version of a non-standalone network (NSN) and a mid-to-late version of a standalone network (SN). It further subdivides it into eight 5G candidate network architectures. The non-standalone network is recognized as the most suitable 5G version for initial deployment. The 5G network integrates other heterogeneous networks to provide higher bandwidth. Among them, the number of small cells will increase by multiples due to their low cost and rapid deployment characteristics. It implies that the density of base stations and the number of base station handovers in heterogeneous networks will increase significantly. However, too many handovers may be caused by the failure to effectively control the ping-pong effect, resulting in a severe drop in system performance. Traditionally, the handover decision is only based on the received signal strength. However, the only handover decision criterion cannot ensure the connection service quality facing the increasingly complex heterogeneous network environment. This paper proposes a new handover mechanism based on the 5G non-standalone networking, including a measuring module, a scoring module, a forwarding module, and a ping-pong effect module. The handover mechanism considers the base station bandwidth, signal strength, UE moving direction, and base station locations. The system will score and classify each candidate base station to reduce the ping-pong effect in a high-density base station environment, thus ensuring that the handover is performed under the commitment of QoS requirements to improve the throughput of user services.
    Relation: 2020 International Computer Symposium (ICS)
    Data Type: conference
    DOI link: 10.1109/ICS51289.2020.00013
    https://doi.org/10.1109/ICS51289.2020.00013
    Appears in Collections:[Department of Computer Science ] Proceedings

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