PBPHS: a profile-based predictive handover strategy for 5G networks

J Sun, Y Zhang, M Trik - Cybernetics and Systems, 2024 - Taylor & Francis
J Sun, Y Zhang, M Trik
Cybernetics and Systems, 2024Taylor & Francis
One of the necessities of mobile networks is uninterrupted access to wireless services,
taking into account the requirements of quality of service. With the development of the fifth-
generation (5G), high improvements will be achieved in several aspects such as seamless
communications, flexibility, scalability, cost-effectiveness, and energy efficiency. For this
purpose, in 5G architecture, a large amount of Small cell Base Stations (SBS) will be
considered to cover all of the users and their equipment devices. Due to the possibility of …
Abstract
One of the necessities of mobile networks is uninterrupted access to wireless services, taking into account the requirements of quality of service. With the development of the fifth-generation (5G), high improvements will be achieved in several aspects such as seamless communications, flexibility, scalability, cost-effectiveness, and energy efficiency. For this purpose, in 5G architecture, a large amount of Small cell Base Stations (SBS) will be considered to cover all of the users and their equipment devices. Due to the possibility of rapid user mobility, the high traffic load on SBS, or any service failure, the handover process will be inevitable. However, there will be several challenges that must be noticed, such as selecting an appropriate SBS for handover in order to improve the quality of service and reduce unnecessary handovers. In this paper, a profile-based predictive handover strategy (PBPHS) is presented to select the best alternative for handover and hence to reduce handovers handovers, according to a predictive model that is based on the previous user’s utilization of resources and mobility tracking. The simulation results depicted the performance of the proposed handover strategy in the handover reduction rate, which on average outperforms the state-of-the-art methods by 13% to 26%.
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