Reliability comparisons of mobile network operators: an experimental case study from a crowdsourced dataset

It is of great interest for Mobile Network Operators MNOs to know how well their network infrastructure performance behaves in different geographical regions of their operating country compared to their horizontal competitors. However, traditional network monitoring and measurement methods of network infrastructure use limited numbers of measurement points that are insufficient for detailed analysis and expensive to scale using an internal workforce. On the other hand, the abundance of crowdsourced content can engender various unforeseen opportunities for MNOs to cope with this scaling problem. This paper investigates end-to-end reliability and packet loss PL performance comparisons of MNOs using a previously collected real-world proprietary crowdsourced dataset from a user application for 13 months’ duration in Turkey. More particularly, a unified crowdsourced data-aided statistical MNO comparison framework is proposed, which consists of data collection and network performance analysis steps. Our results are statistically supported using confidence interval analysis for the mean difference of PL ratios and reliability levels of MNOs using unpaired number of observations statistical analysis. The network performance results indicate that significant performance differences in MNOs depending on different regions of the country exist. Moreover, we observe that the overall comparative ordered list of MNOs’ reliability performance does not differ when both PL and latency requirements vary.

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  • [1] IEEE 5G Initiative Technology Roadmap Working Group. IEEE 5G and Beyond Technology Roadmap. White Paper 2017.
  • [2] 3GPP Technical Specification Group Radio Access Network. Study on scenarios and requirements for next generation access technologies. TR 38.913 V15.0.0 (Release 15) 2018.
  • [3] Benjebbour A, Kitao K, Kakishima Y, Na C. 3GPP defined 5G requirements and evaluation conditions. NTT DOCOMO Technical Journal 2018; 3: 13-23.
  • [4] Faggiani A, Gregori E, Lenzini L, Valerio L, Vecchio A. Smartphone-based crowdsourcing for network monitoring: opportunities, challenges, and a case study. IEEE Communications Magazine 2014; 52 (1): 106-113. doi: 10.1109/MCOM.2014.6710071
  • [5] Zeydan E, Bastug E, Bennis M, Kader MA, Karatepe IA et al. Big data caching for networking: moving from cloud to edge. IEEE Communications Magazine 2016; 54 (9): 36-42. doi: 10.1109/MCOM.2016.7565185
  • [6] Baldo N, Giupponi L, Mangues-Bafalluy J. Big data empowered self organized networks. In: Proceedings of 20th European Wireless Conference; Barcelona, Spain; 2014. pp. 1-8.
  • [7] Narmanlioglu O, Zeydan E, Kandemir M, Kranda T. Prediction of active UE number with Bayesian neural networks for self-organizing LTE networks. In: Proceedings of Network of Future Conference; London, UK; 2017.
  • [8] Bennis M, Debbah M, Poor HV. Ultra reliable and low-latency wireless communication: tail, risk, and scale. Proceedings of the IEEE 2018; 106 (10): 1834-1853. doi: 10.1109/JPROC.2018.2867029
  • [9] Nikravesh A, Choffnes DR, Katz-Bassett E, Mao ZM, Welsh M. Mobile network performance from user devices: a longitudinal, multidimensional analysis. In: Proceedings of International Conference on Passive and Active Network Measurement; Los Angeles, CA, USA; 2014. pp. 12-22.
  • [10] Nikravesh A, Guo Y, Qian F, Mao ZM, Sen S. 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; New York, NY, USA; 2016. pp. 189-201.
  • [11] Arlos P, Fiedler M. Influence of the packet size on the one-way delay in 3G networks. In: Proceedings of International Conference on Passive and Active Network Measurement; Zurich, Switzerland; 2010. pp. 61-70.
  • [12] Celebi OF, Zeydan E, Kurt OF, Dedeoglu O, Ileri O et al. On use of big data for enhancing network coverage analysis. In: Proceedings of International Conference on Telecommunications; Casablanca, Morocco; 2013. pp. 1-5.
  • [13] Birke D, Swann GMP. Network effects and the choice of mobile phone operator. In: Cantner U, Malerba F (editors). Innovation, Industrial Dynamics and Structural Transformation. Berlin, Germany: Springer-Verlag, 2018, pp. 109- 128.
  • [14] Doganoglu T, Grzybowski L. Estimating network effects in mobile telephony in Germany. Information Economics and Policy 2007; 19 (1): 65-79. doi: 10.1016/j.infoecopol.2006.11.001
  • [15] Mancuso V, Quirós MP, Midoglu C, Moulay M, Comite V et al. Results from running an experiment as a service platform for mobile broadband networks in Europe. Computer Communications 2019; 133: 89-101. doi: 10.1016/j.comcom.2018.09.004
  • [16] Xu F, Li Y, Wang H, Zhang P, Jin D. Understanding mobile traffic patterns of large scale cellular towers in urban environment. IEEE/ACM Transactions on Networking (TON) 2017; 25 (2): 1147-1161. doi: 10.1109/TNET.2016.2623950
  • [17] Kousias K, Midoglu C, Alay O, Lutu A, Argyriou A et al. The same, only different: contrasting mobile operator behavior from crowdsourced dataset. In: Proceedings of IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications; Montreal, Canada; 2017. pp. 1-6.
  • [18] Apajalahti K, Walelgne EA, Manner J, Hyvönen E. Correlation-based feature mapping of crowdsourced LTE data. In: Proceedings of IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications; Bologna, Italy; 2018. pp. 1-7.
  • [19] Sonntag S, Manner J, Schulte L. Netradar - measuring the wireless world. In: Proceedings of 11th International Symposium and Workshops on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks; Tsukuba Science City, Japan; 2013. pp. 29-34.
  • [20] Chiaraviglio L, Galán-Jiménez J, Fiore M, Blefari-Melazzi N. Not in my neighborhood: a user equipment perspective of cellular planning under restrictive EMF limits. IEEE Access 2019; 7: 6161-6185. doi: 10.1109/ACCESS.2018.2888916
  • [21] Fida M, Marina MK. Impact of device diversity on crowdsourced mobile coverage maps. In: Proceedings of 14th International Conference on Network and Service Management; Rome, Italy; 2018. pp. 348-352.
  • [22] Qu L, Khabbaz M, Assi C. Reliability-aware service chaining in carrier-grade softwarized networks. IEEE Journal on Selected Areas in Communications 2018; 36 (3): 558-573. doi: 10.1109/JSAC.2018.2815338
  • [23] Petrov V, Lema M, Gapeyenko M, Antonakoglou K, Moltchanov D et al. Achieving end-to-end reliability of missioncritical traffic in softwarized 5G networks. IEEE Journal on Selected Areas in Communications 2018; 36 (3): 485-501. doi: 10.1109/JSAC.2018.2815419
  • [24] Pocovi G, Kolding T, Lauridsen M, Mogensen R, Markmoller C et al. Measurement framework for assessing reliable real-time capabilities of wireless networks. IEEE Communications Magazine 2018; 56 (99): 1-8. doi: 10.1109/MCOM.2018.1800159
  • [25] Huawei. Indoor 5G Networks (version 2.0). White Paper 2018.
  • [26] Yildirim A, Zeydan E, Yigit IO. A statistical comparative performance analysis of mobile network operators. Wireless Networks 2018; 1-20. doi: 10.1007/s11276-018-1837-6
  • [27] Jain R. The Art Of Computer Systems Performance Analysis: Techniques For Experimental Design, Measurement, Simulation, and Modeling. New York, NY, USA: Wiley, 1991.
  • [28] Ji H, Park S, Yeo J, Kim Y, Lee J et al. Ultra-reliable and low-latency communications in 5G downlink: physical layer aspects. IEEE Wireless Communications 2018; 25 (3): 124-130. doi: 10.1109/MWC.2018.1700294
  • [29] Liu J, Wan J, Zeng B, Wang Q, Song H et al. A scalable and quick-response software defined vehicular network assisted by mobile edge computing. IEEE Communications Magazine 2017; 55 (7): 94-100. doi: 10.1109/MCOM.2017.1601150