Wireless Internet. 10th International Conference, WiCON 2017, Tianjin, China, December 16-17, 2017, Proceedings

Research Article

Spatial-Temporal Distribution of Mobile Traffic and Base Station Clustering Based on Urban Function in Cellular Networks

  • @INPROCEEDINGS{10.1007/978-3-319-90802-1_26,
        author={Tong Wang and Xing Zhang and Wenbo Wang},
        title={Spatial-Temporal Distribution of Mobile Traffic and Base Station Clustering Based on Urban Function in Cellular Networks},
        proceedings={Wireless Internet. 10th International Conference, WiCON 2017, Tianjin, China, December 16-17, 2017, Proceedings},
        proceedings_a={WICON},
        year={2018},
        month={5},
        keywords={Spatial-temporal distribution Mobile traffic BS clustering Urban function Application usage pattern},
        doi={10.1007/978-3-319-90802-1_26}
    }
    
  • Tong Wang
    Xing Zhang
    Wenbo Wang
    Year: 2018
    Spatial-Temporal Distribution of Mobile Traffic and Base Station Clustering Based on Urban Function in Cellular Networks
    WICON
    Springer
    DOI: 10.1007/978-3-319-90802-1_26
Tong Wang1,*, Xing Zhang1, Wenbo Wang1
  • 1: Beijing University of Posts and Telecommunications
*Contact email: zqwt199439@bupt.edu.cn

Abstract

With the rapid development of mobile internet, it’s essential to understand the spatial-temporal distribution of mobile traffic. Based on the mobile traffic data collected from a large 4G cellular network in northwestern China, this paper presents detailed analyses of the traffic data on base stations in two aspects: (1) spatial-temporal distribution, (2) clustering based on physical context, i.e., urban function. We introduce the concept of traffic density to measure the traffic level, according to the Voronoi diagram to partition the covering area of BSs. Both spatial and temporal dimensions show distinct inhomogeneity property of mobile traffic. Furthermore, we cluster BSs utilizing urban function information, which enables us to identify and label base stations. The diverse application usage patterns of each cluster of BSs are obtained, which could be applied in resource cache policy and BS loading allocation.