Rethinking Behaviors and Activities of Base Stations in Mobile Cellular Networks Based on Big Data Analysis

• Published in: IEEE transactions on network science and engineering Vol. 7; no. 1; pp. 80 - 90
• Main Authors: Jiang, Dingde, Huo, Liuwei, Song, Houbing
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Base stations; Behavior; Big Data; big data technologies; call detail records; Cellular communication; Cellular networks; Cleaning; Correlation analysis; Cross correlation; Data analysis; Data models; Datasets; Feature extraction; Network behaviors; New technology; Pattern analysis; Poles and towers; predictability; Radio equipment; Stations; Wireless networks
• ISSN: 2327-4697; 2334-329X
• DOI: 10.1109/TNSE.2018.2861388

This paper uses big data technologies to study base stations' behaviors and activities and their predictability in mobile cellular networks. With new technologies quickly appearing, current cellular networks have become more larger, more heterogeneous, and more complex. This provides network managements and designs with larger challenges. How to use network big data to capture cellular network behavior and activity patterns and perform accurate predictions is recently one of main problems. To the end, first we exploit big data platform and technologies to analyze cellular network big data, i.e., Call Detail Records (CDRs). Our CDRs data set, which includes more than 1,000 cellular towers, more than million lines of CDRs, and several million users and sustains for more than 100 days, is collected from a national cellular network. Second, we propose our methodology to analyze these big data. The data pre-handling and cleaning approach is proposed to obtain the valuable big data sets for our further studies. The feature extraction and call predictability methods are presented to capture base stations' behaviors and dissect their predictability. Third, based on our method, we perform the detailed activity pattern analysis, including call distributions, cross correlation features, call behavior patterns, and daily activities. The detailed analysis approaches are also proposed to dig out base stations' activities. A series of findings are found and observed in the analysis process. Finally, a study case is proposed to validate the predictability of base stations' behaviors and activities. Our studies demonstrates that big data technologies can indeed be utilized to effectively capture network behaviors and predict network activities so that they can help perform highly effective network managements.