Abstract
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.
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References
Hu, Y., Liang, S., Fang, Y.: Analysis of cell coverage based on LTE measurement report data. Telecommun. Eng. Technol. Stand. 1, 33–37 (2012)
Harrington, P.: Machine Learning in Action. Manning, Greenwich (2012)
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)
Zhao, G.: Analysis of causes and treatment of uplink interference according to interference threshold. China Informationization, vol. 1672-5158201212-0028-02, p. 28 (2012)
Rahm, E., Do, H.H.: Data cleaning: problems and current approaches. IEEE Data Eng. Bull. 23(4), 3–13 (2000)
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)
Tan, P.N.: Introduction to Data Mining. Pearson Education India, Delhi (2006)
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)
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)
Fang, Y.: Application of measurement report in TD - LTE wireless network optimization. Mobile Commnun. 31–33 (2014)
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)
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)
Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)
Pedregosa, F., Varoquaux, G., Gramfort, A., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Albahari, J., Albahari, B.: C# 5.0 in a Nutshell: The Definitive Reference. O’Reilly Media, Inc., Sebastopol (2012)
Acknowledgment
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.
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Zhang, K., Chuai, G., Gao, W., Liu, X., Ren, Y. (2019). Data Analysis of Measurement Report and Diagnosis of Mobile Network Malfunction Based on K-Means Algorithm. In: Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, vol 463. Springer, Singapore. https://doi.org/10.1007/978-981-10-6571-2_21
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DOI: https://doi.org/10.1007/978-981-10-6571-2_21
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