10th EAI International Conference on Communications and Networking in China

Research Article

Dynamic Clustering Algorithm Design for Ultra Dense Small Cell Networks in 5G

  • @INPROCEEDINGS{10.4108/eai.15-8-2015.2260811,
        author={Siyi Chen and Chengwen Xing and Zesong Fei and Hualei Wang and Zhengang Pan},
        title={Dynamic Clustering Algorithm Design for Ultra Dense Small Cell Networks in 5G},
        proceedings={10th EAI International Conference on Communications and Networking in China},
        publisher={IEEE},
        proceedings_a={CHINACOM},
        year={2015},
        month={9},
        keywords={ultra dense networks clustering algorithms graph-based algorithm},
        doi={10.4108/eai.15-8-2015.2260811}
    }
    
  • Siyi Chen
    Chengwen Xing
    Zesong Fei
    Hualei Wang
    Zhengang Pan
    Year: 2015
    Dynamic Clustering Algorithm Design for Ultra Dense Small Cell Networks in 5G
    CHINACOM
    IEEE
    DOI: 10.4108/eai.15-8-2015.2260811
Siyi Chen1,*, Chengwen Xing1, Zesong Fei1, Hualei Wang2, Zhengang Pan2
  • 1: Beijing Institute of Technology
  • 2: China Mobile Research Institute
*Contact email: chensiyi_bit@163.com

Abstract

Ultra dense networks are a promising technology enabling high power and spectrum efficiencies in future wireless systems. It is well-known that for ultra dense networks inter-cell interference is one of the main bottlenecks prohibiting achieving the promised performance gains. In order to effectively coordinate or mitigate interference in paper, we propose a graph-based low complexity dynamic clustering algorithm. The key idea behind the proposed algorithm is that dividing the whole network into a number of clusters under size constraint and the maximum intra-cluster interference and minimum inter-cluster interference. The logic is maximum intra-cluster can be effectively controlled by the coordination within each cluster. Meanwhile, graph-based algorithm is exploited to further reduce implementation complexity and make the proposed algorithm suitable for practical implementation. Finally, simulation results numerically demonstrate that the proposed low complexity algorithm has almost the same performance compared to the existing high performance algorithm but the complexity is much lower.