Communications and Networking. 12th International Conference, ChinaCom 2017, Xi’an, China, October 10-12, 2017, Proceedings, Part II

Research Article

A Green Load Balancing Algorithm for Dynamic Spatial-Temporal Traffic Distribution in HetNets

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  • @INPROCEEDINGS{10.1007/978-3-319-78139-6_40,
        author={Jichen Jiang and Xi Li and Hong Ji},
        title={A Green Load Balancing Algorithm for Dynamic Spatial-Temporal Traffic Distribution in HetNets},
        proceedings={Communications and Networking. 12th International Conference, ChinaCom 2017, Xi’an, China, October 10-12, 2017, Proceedings, Part II},
        proceedings_a={CHINACOM},
        year={2018},
        month={4},
        keywords={Spatial-temporal variation Load balance Small cell ON/OFF},
        doi={10.1007/978-3-319-78139-6_40}
    }
    
  • Jichen Jiang
    Xi Li
    Hong Ji
    Year: 2018
    A Green Load Balancing Algorithm for Dynamic Spatial-Temporal Traffic Distribution in HetNets
    CHINACOM
    Springer
    DOI: 10.1007/978-3-319-78139-6_40
Jichen Jiang1,*, Xi Li1,*, Hong Ji1,*
  • 1: Beijing University of Posts and Telecommunications
*Contact email: jiangjichen@bupt.edu.cn, lixi@bupt.edu.cn, jihong@bupt.edu.cn

Abstract

With the increasing users demands, the data traffic in the network reveal different characteristics in both spatial and temporal dimensions, bringing severe load imbalance problem. This may impact resource utilization, users experience and system energy efficiency, and then need further investigation. In this paper, we propose a distributed load-balancing algorithm considering this spatial-temporal variation in a two-tier heterogeneous network. Instead of illuminating the spatial-temporal influence, we make use of this characteristic while designing the algorithm, and accordingly switch ON/OFF small cell base stations (SBSs) for improving the energy efficiency. A load factor described with load variance is derived, based on which the problem is formulated as a non-linear integer programming that seeks to minimize a load function. Then a suboptimal solution is obtained by an effective heuristic algorithm. Simulation results show that our proposed algorithm balances the traffic load better and significantly reduces the total energy consumption, compared with conventional load-balancing scheme.