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文章基本信息

  • 标题:Computing on Base Station Behavior Using Erlang Measurement and Call Detail Record
  • 作者:Sihai Zhang ; Dandan Yin ; Yanqin Zhang
  • 期刊名称:IEEE Transactions on Emerging Topics in Computing
  • 印刷版ISSN:2168-6750
  • 出版年度:2015
  • 卷号:3
  • 期号:3
  • 页码:444-453
  • DOI:10.1109/TETC.2015.2389614
  • 语种:English
  • 出版社:IEEE Publishing
  • 摘要:

    With the impressive development of wireless devices and growth of mobile users, telecommunication operators are thirsty for understanding the characteristics of mobile network behavior. Based on the big data generated in the telecommunication networks, telecommunication operators are able to obtain substantial insights using big data analysis and computing techniques. This paper introduces the important aspects in this topic, including data set information, data analysis techniques, and two case studies. We categorize the data set in the telecommunication networks into two types, user-oriented and network-oriented, and discuss the potential application. Then, several important data analysis techniques are summarized and reviewed, from temporal and spatial analysis to data mining and statistical test. Finally, we present two case studies, using Erlang measurement and call detail record, respectively, to understand the base station behavior. Interestingly, the night burst phenomenon of college students is revealed by comparing the base stations location and real-world map, and we conclude that it is not proper to model the voice call arrivals as Poisson process.

  • 关键词:Big Data; data analysis; data mining; mobile computing; stochastic processes; temporal databases; visual databases; Big Data analysis; Erlang measurement; Poisson process; base station behavior; base stations location; call detail record; college students; computing techniques; data analysis techniques; data mining; data set information; mobile network behavior; mobile users; network-oriented data set; night burst phenomenon; real-world map; spatial data analysis; statistical test; telecommunication networks; telecommunication operators; temporal data analysis; user-oriented data set; voice call arrivals; wireless devices; Base stations; Big data; Correlation; Mobile communication; Mobile computing; Noise; Wireless communication; Mobile Communication Networks; Spatial-Temporal Correlation; Traffic Analysis; Wireless Big Data; Wireless big data; mobile communication networks; spatial-temporal correlation; traffic analysis
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