Advertisement

Fault Propagation Models Generation in Mobile Telecommunications Networks Based on Bayesian Networks with Principal Component Analysis Filtering

  • Artur MaździarzEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 945)

Abstract

The mobile telecommunication area has been experiencing huge changes recently. Introduction of new technologies and services (2G, 3G, 4G(LTE)) as well as multivendor environment distributed across the same geographical area bring a lot of challenges in network operation. This explains why effective yet simple tools and methods delivering essential information about network problems to network operators are strongly needed. The paper presents the methodology of generating the so-called fault propagation model which discovers relations between alarm events in mobile telecommunication networks based on Bayesian Networks with Primary Component Analysis pre-filtering. Bayesian Network (BN) is a very popular FPM which also enables graphical interpretation of the analysis. Due to performance issues related to BN generation algorithms, it is advised to use pre-processing phase in this process. Thanks to high processing efficiency for big data sets, the PCA can play the filtering role for generating FPMs based on the BN.

Keywords

Fault Propagation Model (FPM) Primary Component Analysis (PCA) Root Cause Analysis (RCA) Mobile telecommunication network Bayesian Networks 

References

  1. 1.
    Bhaumik, S.K.: Root cause analysis in engineering failures. Trans. Indian Inst. Met. 63(2–3), 297–299 (2010)CrossRefGoogle Scholar
  2. 2.
    Datta, R., Niharika, N.: Comparative study between the generations of mobile communication 2G, 3G & 4G. Int. J. Recent Innov. Trends Comput. Commun. 1(4) (2013). ISSN 2321-8169Google Scholar
  3. 3.
    Górecki, T.: Podstawy statystyki z przykładami w R. BTC, Legionowo (2011). ISBN 978-83-60233-69-6Google Scholar
  4. 4.
    Hardle, W., Simar, L.: Applied Multivariate Statistical Analysis. Springer (2007)Google Scholar
  5. 5.
    Harman, H.: Modern Factor Analysis. University of Chicago Press, Chicago (1975)zbMATHGoogle Scholar
  6. 6.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Springer (2001).  https://doi.org/10.1007/b94608
  7. 7.
    Holmes, D.E., Jain, L.C.: Innovations in Bayesian Networks Theory and Applications (2008). ISBN 978-3-540-85065-6Google Scholar
  8. 8.
    Hong, P., Sen, P.: Incorporating non-deterministic reasoning in managing heterogeneous network faults. In: Krishnan, I., Zimmer, W. ( eds.) Integrated Network Management II, pp. 481–492, PNorth-Holland, Amsterdam (1991)Google Scholar
  9. 9.
    Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley (2001)Google Scholar
  10. 10.
    Jensen, F.V., Nielsen, T.D.: Bayesian Networks and Decision Graphs (2007). ISBN-10: 0-387-68281-3Google Scholar
  11. 11.
    Kim, J.O., Mueller, C.W.: Factor Analysis. Statistical Methods and Practical Issues. Sage Publications, Beverly Hills (1978)Google Scholar
  12. 12.
    Koronacki, J., Ćwik, J.: Statystyczne systemy ucza̧ce siȩ, 2nd edn. EXIT, Warszawa (2008)Google Scholar
  13. 13.
    Lopa, M., Vora, J.: Evolution of Mobile Generation Technology: 1G to 5G and Review of Upcoming Wireless Technology 5G. Int. J. Modern Trends Eng. Res. 121(6) (2015). ISSN 2393-8161Google Scholar
  14. 14.
    Manly, B.F.J.: Multivariate Statistical Methods. A Primer. Chapman and Hall, London (1986)zbMATHGoogle Scholar
  15. 15.
    Maździarz, A.: Fault Propagation Models Generation in Mobile Telecommunications Networks based on Bayesian Networks with Principal Component Analysis Filtering. In: Kulczycki, P., Kowalski, P.A., Lukasik, S. (eds.) Contemporary Computational Science, p. 47. AGH-UST Press, Cracow (2018)Google Scholar
  16. 16.
    Myung, I.J.: Tutorial on maximum likelihood estimation. J. Math. Psychol. 47, 90–100 (2003)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Nagarajan, R., Scutari, M., Lebre, S.: Bayesian Networks in R with Applications in Systems Biology (2013). ISBN 978-1-4614-6445-7CrossRefGoogle Scholar
  18. 18.
    Okoń, J.: Analiza czynnikowa w psychologii. PWN, Warszawa (1960)Google Scholar
  19. 19.
    Pearson, K.: On lines and planes of closest fit to systems of points in space. Phil. Mag. 2, 559–572 (1901)CrossRefGoogle Scholar
  20. 20.
    Pluta, W.: Wielowymiarowa analiza porównawcza w modelowaniu ekonometrycznym. PWN, Warszawa (1986)Google Scholar
  21. 21.
    Rothman, J.: Some considerations affecting the use of factor analysis in market research. J. Mark. Res. Soci. 38(4), 371–381 (1996)Google Scholar
  22. 22.
    Samba, A.: A Network Management Framework for Emerging Telecommunications Networks. Department of Computer Science Kent State University, Kent OH 44242, USA ,Chapter 8 of Modeling and Simulation Tools for Emerging Telecommunication Networks Needs, Trends, Challenges and Solutions (2006). ISBN-10: 0-387-32921-8Google Scholar
  23. 23.
    Singh, K., Thakur, S., Singh, S.: Comparison of 3G and LTE with other Generation. Int. J. Comput. Appl. (0975 - 8887) 121(6) (2015). ISSN 0975-8887CrossRefGoogle Scholar
  24. 24.
    Steinder, M., Sethi, A.S.: A survey of fault localization techniques in computer networks. Sci. Comput. Program. 53, 165–194 (2004)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Sutter, M.T., Zeldin, P.E.: Designing expert systems for real time diagnosis of self-correcting networks. IEEE Netw. 43–51 (1998)CrossRefGoogle Scholar
  26. 26.
    Zakrzewska, M.: Analiza czynnikowa w budowaniu i sprawdzaniu modeli psychologicznych. UAM, Poznań (1994). ISBN-10: 8323204772Google Scholar
  27. 27.
    Zeliaś, A.: Metody Statystyczne. PWN, Warszawa (2000)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Systems Research InstitutePolish Academy of ScienceWarsawPoland

Personalised recommendations