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Telecom Big Data Based User Offloading Self-Optimisation in Heterogeneous Relay Cellular Systems
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Author(s): Lexi Xu (China Unicom Network Technology Research Institute, Beijing, China & Queen Mary University of London, London, United Kingdom), Yuting Luan (The Third Railway Survey and Design Institute Group Corporation, Shenyang, China), Xinzhou Cheng (China Unicom Network Technology Research Institute, Beijing, China), Yifeng Fan (Southeast University, Nanjing, China & Queen Mary University of London, London, United Kingdom), Haijun Zhang (University of Science and Technology Beijing, Beijing, China), Weidong Wang (Beijing University of Posts and Telecommunications, Beijing, China)and Anqi He (Queen Mary University of London, London, United Kingdom)
Copyright: 2017
Volume: 8
Issue: 2
Pages: 20
Source title:
International Journal of Distributed Systems and Technologies (IJDST)
Editor(s)-in-Chief: Nik Bessis (Edge Hill University, UK)
DOI: 10.4018/IJDST.2017040103
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Abstract
This paper proposes a telecom big data based user offloading self-optimisation (TBDUOS) scheme. Its aim is to assist telecom operators to effectively balancing the load distribution with achieving good service performance and customer management in heterogeneous relay cellular systems. To achieve these objectives, in the cell-level offloaded traffic analysis stage, the optimal offloaded traffic is calculated to minimise the total blocking probability. In the user-level offloading stage, the user portrait is drawn and the K-MEANS algorithm is employed to manage the users clustering in the heavily loaded cell, and finally shifting users to assistant cells. Simulation results show the TBDUOS scheme can effectively reduce the handover failure and call dropping of specific users, especially voice/stream users, high consumption users, high level users. The TBDUOS scheme can also reduce the blocking probability.
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