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

Research Article

A Tractable Traffic-Aware User Association Scheme in Heterogeneous Networks

  • @INPROCEEDINGS{10.1007/978-3-319-66628-0_21,
        author={Xiaobing Lin and Kun Yang and Xing Zhang},
        title={A Tractable Traffic-Aware User Association Scheme in Heterogeneous Networks},
        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={Traffic demand User association Cell range expansion System capacity Heterogeneous networks},
        doi={10.1007/978-3-319-66628-0_21}
    }
    
  • Xiaobing Lin
    Kun Yang
    Xing Zhang
    Year: 2017
    A Tractable Traffic-Aware User Association Scheme in Heterogeneous Networks
    CHINACOM
    Springer
    DOI: 10.1007/978-3-319-66628-0_21
Xiaobing Lin1, Kun Yang1, Xing Zhang1,*
  • 1: Beijing University of Posts and Telecommunications
*Contact email: hszhang@bupt.edu.cn

Abstract

In Heterogeneous networks (HetNets), the power difference between macro base stations (MBSs) and small base stations (SBSs) causes severe load unbalance. Therefore, cell range expansion (CRE) is proposed as an effective method to extend the coverage of SBSs and achieve balanced utilization of BSs. However, the downlink (DL) quality for offloaded user equipment (UE) cannot be guaranteed. In this paper, a traffic-aware user association scheme is proposed in HetNets. Distinct association biases are applied to different UEs according to their requirements. System performance of the proposed scheme is analyzed using the tool of stochastic geometry. The results show that the proposed scheme can improve DL throughput by enhancing the rate coverage of UEs, meanwhile signal-to-interference-plus-noise ratio (SINR) requirement with low data rate demand UEs is ensured. Moreover, the optimal association bias, which maximizes DL throughput, can be derived through particle swarm optimization (PSO), and it changes with different densities of BSs and UEs.