Communications and Networking. 11th EAI international Conference, ChinaCom 2016 Chongqing, China, September 24-26, 2016, Proceedings, Part II

Research Article

Dynamic APs Grouping Scheme Base on Energy Efficiency in UUDN

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  • @INPROCEEDINGS{10.1007/978-3-319-66628-0_7,
        author={Shanshan Yu and Xi Li and Hong Ji and Yiming Liu},
        title={Dynamic APs Grouping Scheme Base on Energy Efficiency in UUDN},
        proceedings={Communications and Networking. 11th EAI international Conference, ChinaCom 2016 Chongqing, China, September 24-26, 2016, Proceedings, Part II},
        proceedings_a={CHINACOM},
        year={2017},
        month={10},
        keywords={UDN User-centric Dynamic APs grouping Energy efficiency},
        doi={10.1007/978-3-319-66628-0_7}
    }
    
  • Shanshan Yu
    Xi Li
    Hong Ji
    Yiming Liu
    Year: 2017
    Dynamic APs Grouping Scheme Base on Energy Efficiency in UUDN
    CHINACOM
    Springer
    DOI: 10.1007/978-3-319-66628-0_7
Shanshan Yu1,*, Xi Li1,*, Hong Ji1,*, Yiming Liu1,*
  • 1: Beijing University of Posts and Telecommunications
*Contact email: yushanshan@bupt.edu.cn, lixi@bupt.edu.cn, jihong@bupt.edu.cn, liuyiming@bupt.edu.cn

Abstract

Ultra dense Network (UDN) is considered as a promising technology for 5G. With dense access points (APs), one user can be served by several APs cooperatively. Hence, how to choose APs and group them is a big challenge. In this paper, a dynamic APs grouping scheme is proposed for the downlink of User-centric UDN (UUDN). This scheme takes terrain and network topology into consideration to divide the APs into several available candidate sets (ACSs). The APs can be chosen from the ACS as the group member for UE’s APs group (APG). Once the service requirement changes or user moves, the group should be changed accordingly. The optimal objective is maximum energy efficiency under the constraints of transmission power and user’s data rate requirements. This scheme solves the problem of AP selection and power allocation. It is modeled as a discrete mixed combinational optimization problem, and a quantum-behaved particle swarm optimization (QPSO) algorithm is adopted to solve it efficiently. In addition, simulation results have also proved the effectiveness and flexibility of the proposed scheme.