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A statistical comparative performance analysis of mobile network operators

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Abstract

Mobile telephony is one of the most widely utilized technologies in the modern world. Records of the usage behaviour of mobile users can provide valuable information for understanding the behaviour of networks for Mobile Network Operators (MNOs). For different reasons, MNOs are interested in knowing how their competitors’ performance varies based on location, phone category, phone Operating System (OS) for various cellular network technology (CNT). This can help MNOs to invest intelligently in locations where they operate with inferior performance. Therefore, Key Performance Indicator (KPI) comparisons among MNOs are of interest for all MNOs. In this article, we investigate cellular network performance statistical comparisons of major Mobile Network Operators (MNOs) in Turkey using a large scale real-world proprietary mobile traffic dataset over a period of 18 months. Focusing our approach on different dimensions of crowd-sourced dataset allows us: (i) to know end-to-end nationwide network performance comparisons of MNOs using real-world measurement data, (ii) to calculate Confidence Intervals (CIs) for the mean difference of KPIs (such as downlink speed, latency, jitter and packet loss) for obtaining useful comparative statistical information of MNO performances and (iii) to observe the existence of significant performance differences between MNOs depending on the region which they are operating, phone category, phone OS as well as CNTs.

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References

  1. Zeydan, E., Bastug, E., Bennis, M., Kader, M. A., Karatepe, I. A., Er, S. A., et al. (2016). Big data caching for networking: Moving from cloud to edge. IEEE Communications Magazine, 54(9), 36–42.

    Article  Google Scholar 

  2. Naboulsi, D., Fiore, M., Ribot, S., & Stanica, R. (2016). Large-scale mobile traffic analysis: A survey. IEEE Communications Surveys & Tutorials, 18(1), 124–161.

    Article  Google Scholar 

  3. Sanchez, M. I., Zeydan, E., de la Oliva, A., Tan, A. S., Yabas, U., & Bernardos, C. J. (2016). Mobility management: Deployment and adaptability aspects through mobile data traffic analysis. Computer Communications, 95, 3–14.

    Article  Google Scholar 

  4. Furno, A., Fiore, M., & Stanica, R. (2017). Joint spatial and temporal classification of mobile traffic demands. In INFOCOM–36th annual IEEE international conference on computer communications.

  5. Celebi, O. F., Zeydan, E., Kurt, O. F., Dedeoglu, O. Ileri, O., AykutSungur, B., Akan, A., & Ergut, S. (2013). On use of big data for enhancing network coverage analysis. In 2013 20th international conference on telecommunications (ICT), (pp. 1–5). IEEE.

  6. Baldo, N., Giupponi, L., & Mangues-Bafalluy, J. (2014). Big data empowered self organized networks. In Proceedings of the European wireless 2014, 20th European wireless conference (pp. 1–8). VDE.

  7. Nikravesh, A., Choffnes, D. R., Katz-Bassett, E., Mao, Z. M., & Welsh, M. (2014). Mobile network performance from user devices: A longitudinal, multidimensional analysis. In PAM, (vol. 14, pp. 12–22). Springer, Berlin.

  8. Nikravesh, A. Guo, Y., Qian, F., Mao, Z. M., & Sen, S. (2016). An in-depth understanding of multipath tcp on mobile devices: Measurement and system design. In Proceedings of the 22nd annual international conference on mobile computing and networking (pp. 189–201). ACM.

  9. Arlos, P., & Fiedler, M. (2010). Influence of the packet size on the one-way delay in 3g networks. In International conference on passive and active network measurement (pp. 61–70). Springer, Berlin.

  10. Fengli, X., Li, Y., Wang, H., Zhang, P., & Jin, D. (2017). Understanding mobile traffic patterns of large scale cellular towers in urban environment. IEEE/ACM Transactions on Networking (TON), 25(2), 1147–1161.

    Article  Google Scholar 

  11. Furno, A., Stanica, R., & Fiore, M. (2015). A comparative evaluation of urban fabric detection techniques based on mobile traffic data. In Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015,(pp. 689–696). ACM.

  12. Finley, B., Boz, E., Kilkki, K., Manner, J., Oulasvirta, A., & Hämmäinen, H. (2017). Does network quality matter? A field study of mobile user satisfaction. Pervasive and Mobile Computing, 39, 80–99.

    Article  Google Scholar 

  13. Baltrunas, D., Elmokashfi, A., & Kvalbein, A. (2014). Measuring the reliability of mobile broadband networks. In Proceedings of the 2014 conference on internet measurement conference (pp. 45–58). ACM.

  14. Zarinni, F., Chakraborty, A., Sekar, V., Das, S. R., & Gill, P. (2014). A first look at performance in mobile virtual network operators. In Proceedings of the 2014 conference on internet measurement conference (pp. 165–172). ACM.

  15. Pandas: Python data analysis library. (2017). http://pandas.pydata.org/. Accessed 17 Oct 2017.

  16. Folium: Python data visualization. (2017). https://folium.readthedocs.io/en/latest/. Accessed 06 Dec 2017.

  17. Leaflet: An open-source JavaScript library. (2017). http://leafletjs.com/. Accessed 06 Dec 2017.

  18. Jain, R. (1990). The art of computer systems performance analysis: Techniques for experimental design, measurement, simulation, and modeling. New york: Wiley.

    Google Scholar 

  19. GSMA: The GSM Association. https://www.gsmaintelligence.com/. (2018). Accessed 18 April 2018.

  20. Seaborn: statistical data visualization. (2017). https://seaborn.pydata.org/. Accessed 20 Oct 2017.

  21. 3GPP Release 8. (2016). Evolved universal terrestrial radio access (e-utra); user equipment (ue) radio access capabilities. In 3GPP TS36.306

  22. Bland, J. M., & Altman, D. G. (1995). Multiple significance tests: the bonferroni method. Bmj, 310(6973), 170.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Bogazici University, Istanbul, Turkey

    Ahmet Yildirim

  2. Türk Telekom Labs, Istanbul, Turkey

    Engin Zeydan & Ibrahim Onuralp Yigit

Authors
  1. Ahmet Yildirim
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  2. Engin Zeydan
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  3. Ibrahim Onuralp Yigit
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Correspondence to Engin Zeydan.

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Yildirim, A., Zeydan, E. & Yigit, I.O. A statistical comparative performance analysis of mobile network operators. Wireless Netw 26, 1105–1124 (2020). https://doi.org/10.1007/s11276-018-1837-6

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  • DOI: https://doi.org/10.1007/s11276-018-1837-6

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