A Machine Learning Based Temporary Base Station (BS) Placement Scheme in Booming Customers Circumstance

  • Qinglong DaiEmail author
  • Li Zhu
  • Peng Wang
  • Guodong Li
  • Jianjun Chen
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 262)


Explosive increase of terminal users and the amount of data traffic give a great challenge for Internet service providers (ISPs). At the same time, this big data also brings an opportunity for ISPs. How to solve network planning problem in emergency or clogging situation, based on big data? In this paper, we try to realize effective and flexible temporary base station (BS) placement through machine learning in a booming customers situation, with ISPs’ massive data. A machine learning based temporary BS placement scheme is presented. A K-means based model training algorithm is put forward, as a vital part of machine learning based temporary BS placement scheme. K-means algorithm is selected as a representative example of machine learning algorithm. The performances of BS position with random starting point, BS position iteration, average path length with different parameters, are conducted to prove the availability of our work.


Network planning Machine learning Big data K-means algorithm 


  1. 1.
    Key ict indicators for developed and developing countries and the world (totals and penetration rates). Technical report, ITU-T, Geneva, Switzerland (2017)Google Scholar
  2. 2.
    The 41st china statistical report on internet development. Technical report, China Internet Network Information Center, Beijing, China (2018)Google Scholar
  3. 3.
    Mobile internet usage worldwide - statistics and facts. Technical report, Statista, Hamburg, Germany (2018)Google Scholar
  4. 4.
    Alpaydin, E.: Introduction to Machine Learning, 2nd edn. The MIT Press, USA (2009)zbMATHGoogle Scholar
  5. 5.
    Alsheikh, M.A., Lin, S., Niyato, D., Tan, H.P.: Machine learning in wireless sensor networks: algorithms, strategies, and applications. IEEE Commun. Surv. Tutor. 16(4), 1996–2018 (2014). FourthquarterCrossRefGoogle Scholar
  6. 6.
    da Costa, V.G.T., Barbon, S., Miani, R.S., Rodrigues, J.J.P.C., Zarpelao, B.B.: Detecting mobile botnets through machine learning and system calls analysis. In: 2017 IEEE International Conference on Communications (ICC). pp. 1–6 (2017).
  7. 7.
    Curry, E., Hasan, S., Kouroupetroglou, C., Fabritius, W., ul Hassan, U., Derguech, W.: Internet of things enhanced user experience for smart water and energy management. IEEE Internet Comput. 22(1), 18–28 (2018). Scholar
  8. 8.
    Dargie, W., Poellabauer, C.: Localization. Wiley, New York (2010)Google Scholar
  9. 9.
    Klaine, P.V., Imran, M.A., Onireti, O., Souza, R.D.: A survey of machine learning techniques applied to self-organizing cellular networks. IEEE Commun. Surv. Tutor. 19(4), 2392–2431 (2017). FourthquarterCrossRefGoogle Scholar
  10. 10.
    LHeureux, A., Grolinger, K., Elyamany, H.F., Capretz, M.A.M.: Challenges and approaches. IEEE Access 5, 7776–7797 (2017). Scholar
  11. 11.
    Li, W., Meng, W.: An empirical study on email classification using supervised machine learning in real environments. In: 2015 IEEE International Conference on Communications (ICC), pp. 7438–7443 (2015).
  12. 12.
    Lin, Y.T., Oliveira, E.M.R., Jemaa, S.B., Elayoubi, S.E.: Machine learning for predicting qoe of video streaming in mobile networks. In: 2017 IEEE International Conference on Communications (ICC), pp. 1–6 (2017).
  13. 13.
    Liu, L., Cheng, Y., Cai, L., Zhou, S., Niu, Z.: Deep learning based optimization in wireless network. In: 2017 IEEE International Conference on Communications (ICC), pp. 1–6 (2017).
  14. 14.
    Liu, Y., Zhou, C., Cheng, Y.: Integrated bs/onu placement in hybrid epon-wimax access networks. In: GLOBECOM 2009–2009 IEEE Global Telecommunications Conference, pp. 1–6 (2009).
  15. 15.
    Mohammadi, M., Al-Fuqaha, A.: Enabling cognitive smart cities using big data and machine learning: Approaches and challenges. IEEE Commun. Mag. 56(2), 94–101 (2018). Scholar
  16. 16.
    Nguyen, T.T.T., Armitage, G.: A survey of techniques for internet traffic classification using machine learning. IEEE Commun. Surv. Tutor. 10(4), 56–76 (2008). FourthCrossRefGoogle Scholar
  17. 17.
    Rottondi, C., Barletta, L., Giusti, A., Tornatore, M.: Machine-learning method for quality of transmission prediction of unestablished lightpaths. IEEE/OSA J. Opt. Commun. Netw. 10(2), A286–A297 (2018). Scholar
  18. 18.
    Samuel, A.L.: Some studies in machine learning using the game of checkers. Ibm J. Res. Dev. 3(3), 210–229 (1959)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Taylor, D.: Battle of the data science venn diagrams (2016)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Qinglong Dai
    • 1
    Email author
  • Li Zhu
    • 2
    • 3
  • Peng Wang
    • 1
  • Guodong Li
    • 1
  • Jianjun Chen
    • 1
  1. 1.China Academy of Electronics and Information TechnologyBeijingChina
  2. 2.China Transport Telecommunications and Information CenterBeijingChina
  3. 3.People’s Public Security University of ChinaBeijingChina

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