Skip to main content
SpringerLink
Log in

Unsupervised Performance Functions for Wireless Self-Organising Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Traditionally, in cellular networks, troubleshooting experts have manually analyzed Key Performance Indicators (KPI), so that they could identify the cause of problems and fix them. With the emergence of Self-Organizing Networks, Self-Healing systems are designed to automate those troubleshooting tasks. With that aim, the behavior of the KPIs (i.e. their profile under normal and abnormal conditions) needs to be modeled. Since the behavior of the KPIs is network-dependent and it changes as the network evolves, their profile should be automatically defined and readjusted depending on the characteristics of the network. Therefore, in this letter, an automatic process to model the KPIs based on the real data taken from the network is proposed. In particular, this method is characterized by designing a pair of functions (named performance functions) from the statistical behavior of real data without requiring any information about the existence of faults (i.e. unsupervised learning). Results have shown the reliability and effectiveness of the proposed method in comparison to reference approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. 3GPP, Technical Specification Group Services and System Aspects; Telecommunication Management; Self-Organizing Networks (SON); Concepts and requirements, version 13.0.0 (2016-02) , 3rd Generation Partnership Project (3GPP), TS 32.500.

  2. 3GPP, Telecommunication management; Self-Organizing Networks (SON); Self-healing concepts and requirements, version 13.0.0 (2016-02), 3rd Generation Partnership Project (3GPP), TS 32.541.

  3. Barco, R., Lázaro, P., & Muñoz, P. (2012). A unified framework for self-healing in wireless networks. IEEE Communications Magazine, 50, 134–142.

    Article  Google Scholar 

  4. Gómez-Andrades, A., Muñoz, P., Khatib, E. J., de-la-Bandera, I., Serrano, I., & Barco, R. (2015). Methodology for the design and evaluation of self-healing LTE networks. IEEE Transactions on Vehicular Technology. doi:10.1109/TVT.2015.2477945.

    Google Scholar 

  5. Khatib, E. J., Barco, R., Gómez-Andrades, A., & Serrano, I. (2016). Diagnosis based on genetic fuzzy algorithms for LTE self-healing. IEEE Transactions on Vehicular Technology, 65(3), 1639–1651. doi:10.1109/TVT.2015.2414296.

    Article  Google Scholar 

  6. Zimmermann, H.-J. (2010). Fuzzy set theory. Wiley Interdisciplinary Reviews: Computational Statistics, 2(3), 317–332.

    Article  Google Scholar 

  7. Szilágyi, P., & Nováczki, S. (2012). An automatic detection and diagnosis framework for mobile communication systems. IEEE Transactions on Network and Service Management, 9(2), 184–197.

    Article  Google Scholar 

  8. Barco, R., Díez, L., Wille, V., & Lázaro, P. (2009). Automatic diagnosis of mobile communication networks under imprecise parameters. Expert Systems with Applications, 36, 489–500.

    Article  Google Scholar 

  9. Barco, R., Lázaro, P., Wille, V., Díez, L., & Patel, S. (2009). Knowledge acquisition for diagnosis model in wireless networks. Expert Systems with Applications, 36(3), 4745–4752.

    Article  Google Scholar 

  10. Bilgic, T., & Turksen, I. B. (2000). Measurement of membership functions: Theoretical and empirical work. In D. Dubois & H. Prade (Eds.), Fundamentals of fuzzy sets (pp. 195–227). Boston, MA: Springer.

    Chapter  Google Scholar 

  11. Derbel, I., Hachani, N., & Ounelli, H. (2008). Membership functions generation based on density function. In D. Dubois & H Prade (Eds.), International conference on computational intelligence and security (Vol. 1, pp. 96–101).

  12. Fu, A. W.-C., Wong, M. H., Sze, S. C., Wong, W. C., Wong, W. L., & Yu, W. K. Finding fuzzy sets for the mining of fuzzy association rules for numerical attributes. In International symposium on intelligent data engineering and learning, Hong Kong (pp. 263–268).

  13. Bowman, A. W., & Azzalini, A. (1997). Applied smoothing techniques for data analysis: The kernel approach with S-Plus illustrations. Oxford statistical science series. New York, NY: Oxford University Press.

    MATH  Google Scholar 

  14. Casella, G., & Berger, R. L. (2001). Statistical inference (2nd ed.). Belmont: Duxbury.

    MATH  Google Scholar 

  15. Lang, T. A., & Secic, M. (1997). How to report statistics in medicine. Philadelphia: American College of Physicians.

    Google Scholar 

  16. Rea, L. (1997). Designing and conducting survey research (2nd ed.). New York: Jossey-Bass.

    Google Scholar 

  17. Muñoz, P., de la Bandera, I., Ruiz, F., Luna-Ramírez, S., Barco, R., Toril, M., et al. (2011). Computationally-efficient design of a dynamic system-level LTE simulator. International Journal of Electronics and Telecommunications, 57(3), 347–358.

    Article  Google Scholar 

  18. Gómez-Andrades, A., Muñoz, P., Serrano, I., & Barco, R. (2016). Automatic root cause analysis for LTE networks based on unsupervised techniques. IEEE Transactions on Vehicular Technology, 65(4), 2369–2386. doi:10.1109/TVT.2015.2431742.

    Article  Google Scholar 

  19. Creative Research System. The survey system: Sample size calculator. Available: http://www.surveysystem.com/sscalc.htm

  20. Makrehchi, M., Basir, O., & Kamel, M. (2003). Generation of fuzzy membership function using information theory measures and genetic algorithm. In International fuzzy systems association world congress conference on fuzzy sets and systems (pp. 603–610).

  21. Paetz, J. (2002). A note on core regions of membership functions. In European network of excellence on intelligent technologies for smart adaptive systems (pp. 167–173).

Download references

Acknowledgments

This work has been partially funded by Optimi-Ericsson, Junta de Andalucía (Agencia IDEA, Consejería de Ciencia, Innovación y Empresa, Ref. 59288; and Proyecto de Investigación de Excelencia P12-TIC-2905) and ERDF.

Author information

Authors and Affiliations

  1. Universidad de Málaga, 29071, Málaga, Spain

    Ana Gómez-Andrades, Raquel Barco & Pablo Muñoz

  2. Ericsson, 29590, Campanillas, Málaga, Spain

    Inmaculada Serrano

Authors
  1. Ana Gómez-Andrades
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raquel Barco
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pablo Muñoz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Inmaculada Serrano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ana Gómez-Andrades.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gómez-Andrades, A., Barco, R., Muñoz, P. et al. Unsupervised Performance Functions for Wireless Self-Organising Networks. Wireless Pers Commun 90, 2017–2032 (2016). https://doi.org/10.1007/s11277-016-3435-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-016-3435-1

Keywords

Search

Navigation