Data Analysis of Measurement Report and Diagnosis of Mobile Network Malfunction Based on K-Means Algorithm

  • Kaisa ZhangEmail author
  • Gang Chuai
  • Weidong Gao
  • Xuewen Liu
  • Yifang Ren
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 463)


With the rapid development of mobile networks, the number of mobile subscriptions has continued to increase. To efficiently assign mobile network resources, the network operator needs to process and analyze information and statistics about each base station and the traffic that passes through it. This paper presents an application of data analytic by focusing on processing and analyzing datasets from MR (measurement report) data form the actual mobile network. An analysis method based on k-means algorithm for the main service cell uplink SINR (Signal to Interference plus Noise Ratio) analysis of the base station is presented. The analysis of MR data includes data cleaning and K-means algorithm. The purpose of data cleaning is to remove duplicate information, correct existing errors and provide the data consistency. The K-means is an algorithm used for clustering the main service cell uplink SINR in MR data. Finally, through the simulation results, The reason for the malfunction of the base station is obtained. The result can provide support for network optimization and maintenance.


Measurement report Uplink SINR Data cleaning Clustering K-means Malfunction analysis 



This work was funded by National Science and Technology Major Project No. 2016ZX03001009-003 and 2017 Beijing University of Posts and Telecommunications youth research and innovation project. The authors would like to thank our lab for providing the network optimization software, from which the map information was obtained.


  1. 1.
    Hu, Y., Liang, S., Fang, Y.: Analysis of cell coverage based on LTE measurement report data. Telecommun. Eng. Technol. Stand. 1, 33–37 (2012)Google Scholar
  2. 2.
    Harrington, P.: Machine Learning in Action. Manning, Greenwich (2012)Google Scholar
  3. 3.
    Kapil, S., Chawla, M., Ansari, M.D.: On K-means data clustering algorithm with genetic algorithm. In: 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), pp. 202–206. IEEE (2016)Google Scholar
  4. 4.
    Zhao, G.: Analysis of causes and treatment of uplink interference according to interference threshold. China Informationization, vol. 1672-5158201212-0028-02, p. 28 (2012)Google Scholar
  5. 5.
    Rahm, E., Do, H.H.: Data cleaning: problems and current approaches. IEEE Data Eng. Bull. 23(4), 3–13 (2000)Google Scholar
  6. 6.
    Si, M., Lung, C.H., Ajila, S., et al.: An empirical investigation of mobile network traffic data for resource management. In: 2016 IEEE International Congress on Big Data (BigData Congress), pp. 291–298. IEEE (2016)Google Scholar
  7. 7.
    Tan, P.N.: Introduction to Data Mining. Pearson Education India, Delhi (2006)Google Scholar
  8. 8.
    Gao, J., Cheng, X., Xu, L., et al.: An interference management algorithm using big data analytics in LTE cellular networks. In: 16th International Symposium on Communications and Information Technologies (ISCIT), pp. 246–251. IEEE (2016)Google Scholar
  9. 9.
    Shen, C., Luo, J., Xiang, S.: TD-LTE Digital Cell Mobile Communications Network OMC-R Measurement Report Technical Specification, 1st edn., pp. 24–26. China Mobile Communications Corporation, Beijing (2017)Google Scholar
  10. 10.
    Fang, Y.: Application of measurement report in TD - LTE wireless network optimization. Mobile Commnun. 31–33 (2014)Google Scholar
  11. 11.
    Gupta, A., Mehrotra, A., Khan, P.M.: Challenges of cloud computing and big data analytics. In: 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 1112–1115. IEEE (2015)Google Scholar
  12. 12.
    Dash, B., Mishra, D., Rath, A., et al.: A hybridized K-means clustering approach for high dimensional dataset. Int. J. Eng. Sci. Technol. 2(2), 59–66 (2010)Google Scholar
  13. 13.
    Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)Google Scholar
  14. 14.
    Pedregosa, F., Varoquaux, G., Gramfort, A., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)Google Scholar
  15. 15.
    Albahari, J., Albahari, B.: C# 5.0 in a Nutshell: The Definitive Reference. O’Reilly Media, Inc., Sebastopol (2012)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Kaisa Zhang
    • 1
    Email author
  • Gang Chuai
    • 1
  • Weidong Gao
    • 1
  • Xuewen Liu
    • 1
  • Yifang Ren
    • 1
  1. 1.Key Laboratory of Universal Wireless Communications, Ministry of EducationBeijing University of Posts and TelecommunicationsBeijingChina

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