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Cellular network quality evaluation at a university campus on the eve of 5G

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EN
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EN
Thanks to the availability of mobile devices and the spread of broadband access around the world, the number of network users continues to grow. This has raised user awareness when it comes to the quality of content they consume. Many service providers and operators focus on monitoring QoN (Quality of Network) and QoS (Quality of Service) parameters, particularly those influenced by bandwidth and latency. However, for most end-users, quality is understood as the subjective QoE (Quality of Experience), a mixture of several individual factors. This paper presents a QoN evaluation, carried out under real-time operating conditions at a university campus, considering the overall performance of the cellular network. The study involved 50 mobile devices, i.e., smartphones, used by students during their typical activity throughout a week. The results were gathered on the eve of 5G, using a custom-built Android application. This application enabled the acquisition of valuable data about the wireless link, including download and upload speed, latency (pink), the ID of the serving base station, and type of cellular connection. This approach can be used to detect the strengths and weaknesses of back-end networks.
Rocznik
Strony
73--80
Opis fizyczny
Bibliogr. 32 poz., rys., tab.
Twórcy
  • Gdansk University of Technology, Faculty of Electronics, Telecommunications and Informatics 11/12 Narutowicza St., 80-233 Gdansk, Polan
autor
  • Maritime University of Szczecin, Faculty of Transport Engineering and Economics 1-2 Wały Chrobrego St., 70-500 Szczecin, Poland
Bibliografia
  • 1. 3GPP (2005) 3GPP Technical Specification 25.346. Introduction of the multimedia broadcast/multicast service (MBMS) in the radio access network (RAN) stage 2.
  • 2. Afolabi, R.O., Dadlani, A. & Kim, K. (2013) Multicast scheduling and resource allocation algorithms for OFDMA-based systems: A survey. IEEE Communications Surveys & Tutorials 15, 1, pp. 240–254.
  • 3. Atxutegi, E., Liberal, F., Saiz, E. & Ibarrola, E. (2016) Toward standardized internet speed measurements for end users: current technical constraints. IEEE Communications Magazine 54, 9, pp. 50–57.
  • 4. Boz, E., Finley, B., Oulasvirta, A., Kilkki, K. & Manner, J. (2019) Mobile QoE prediction in the field. Pervasive and Mobile Computing 59, 101039, doi: 10.1016/j. pmcj.2019.101039.
  • 5. Chruszczyk, Ł. & Zając, A. (2016) Comparison of indoor/ outdoor, RSSI-based positioning using 433, 868 or 2400 MHz ISM bands. International Journal of Electronics and Telecommunications 62, 4, pp. 395–399.
  • 6. Dymarski, P.G. (2020) Low delay sparse and mixed excitation CELP coders for wideband speech coding. International Journal of Electronics and Telecommunications 66, 1, pp. 69–76.
  • 7. ETSI (2011a) ETSI Technical Specification 102 250-2. Speech and multimedia Transmission Quality (STQ); QoS aspects for popular services in mobile networks; Part 2: Definition of Quality of Service parameters and their computation.
  • 8. ETSI (2011b) ETSI Technical Specification 102 250-3. Speech and multimedia transmission quality (STQ); QoS aspects for popular services in mobile network; Part 3: Typical procedures for Quality of Service measurement equipment.
  • 9. Falkowski-Gilski, P. & Uhl, T. (2020) Current trends in consumption of multimedia content using online streaming platforms: a user-centric survey. Computer Science Review 37, 100268, doi: 10.1016/j.cosrev.2020.100268.
  • 10. GUT (2022) Gdansk University of Technology. Campus of GUT. [Online] Available from: https://campus.pg.edu.pl/ [Accessed: May 15, 2022].
  • 11. Hoppe, C. & Uhl, T. (2020) DASH technology through video streaming over IP using the RTP and HTTP protocols: A comparative study. Journal of Telecommunications and Information Technology 1, pp. 70–75.
  • 12. Ibrahim, S.K. & Khamiss, N.N. (2019) A new wireless generation technology for video streaming. Journal of Computer Networks and Communications, pp. 1–9, doi: 10.1155/2019/3671826.
  • 13. ISO/IEC (2014) ISO/IEC Standard 23009-1. Information technology – dynamic adaptive streaming over HTTP (DASH) – Part 1: Media presentation description and segment formats.
  • 14. ITU-T (2001) ITU-T Recommendation G.1010. End-user multimedia QoS categories.
  • 15. ITU-T (2011) ITU-T Recommendation Y.1541. Network performance objectives for IP-based services.
  • 16. Jeena Jacob, I., Kolandapalayam Shanmugam, S., Piramuthu, S. & Falkowski-Gilski, P. (eds.). (2020) Data Intelligence and Cognitive Informatics. Proceedings of ICDICI 2020, Springer.
  • 17. JEU (2009a) Journal of The European Union L 337/11. European Commission Directive 2009/136/EC.
  • 18. JEU (2009b) Journal of The European Union L 337/37. European Commission: Directive 2009/140/EC.
  • 19. Jiang, T., Song, L. & Zhang, Y. (2010) Orthogonal frequency division multiple access fundamentals and applications. Auerbach Publications.
  • 20. Leszczuk, M., Hanusiak, M., Farias, M., Wyckens, E. & Heston, G. (2016) Recent developments in visual quality monitoring by key performance indicators. Multimedia Tools and Applications 75, pp. 10745–10767.
  • 21. Leszczuk, M. & Janowski, L. (2021) Selected aspects of the new recommendation on subjective methods of assessing video quality in recognition tasks. In Paszkiel, S. (ed.). Control, computer engineering and neuroscience. ICBCI 2021, Springer, pp. 246–254.
  • 22. Letaief, K. & Zhang, Y.J. (2006) Dynamic multiuser resource allocation and adaptation for wireless systems. IEEE Transactions on Wireless Communications 13, 4, pp. 38–47.
  • 23. Nowicki, K. & Uhl, T. (2017) QoS/QoE in the heterogeneous Internet of things (IoT). In Batalla, Jm., Mastorakis, G., Mavromoustakis, C., Pallis, E. (eds.). Beyond the Internet of Things. Everything Interconnected. Springer, pp. 165– 196.
  • 24. Prasad, R., Dovrolis, C., Murray, M. & Claffy, K. (2003) Bandwidth estimation: metrics, measurement techniques and tools. IEEE Network 17, 6, pp. 27–35.
  • 25. Qamar, F., Hindia, MHD N., Abbas, T., Bin Dimyati, K. & Amiri, I.S. (2019) Investigation of QoS performance evaluation over 5G network for indoor environment at millimeter wave bands. International Journal of Electronics and Telecommunications 65, 1, pp. 95–101.
  • 26. Sequeira, L., Fernández-Navajas, J. & Saldana, J. (2014) The effect of the buffer size in QoS for multimedia and bursty traffic: When an upgrade becomes a downgrade. KSII Transactions on Internet and Information Systems 8, 9, pp. 3159–3176.
  • 27. Sule, P. & Joshi, A. (2014) Architectural shift from 4G to 5G wireless mobile networks. International Journal of Computer Science and Mobile Computing 3, 9, pp. 715–721.
  • 28. Uhl, T. & Jürgensen, H. (2015) The new, parameterized IPTV model for determining quality of video streaming in the IPTV service. Bulletin of the Polish Academy of Sciences: Technical Sciences 63, 2, pp. 495–500.
  • 29. Uhrina, M., Frnda, J., Sevcik, L. & Vaculik, M. (2014) Impact of H.264/AVC and H.265/HEVC compression standards on the video quality for 4K resolution. Advances in Electrical and Electronic Engineering 14, 4, pp. 368376.
  • 30. Wasilewska, M., Bogucka, H. & Kliks, A. (2022) Federated Learning for 5G Radio Spectrum Sensing. Sensors 22, 1, 198, doi: 10.3390/s22010198.
  • 31. Xu, T. & Ma, L. (2015) Predictive prefetching for MPEG DASH over LTE networks. IEEE International Conference on Image Processing (ICIP), doi: 10.1109/ ICIP.2015.7351441.
  • 32. Yun, S.H., Kim, Y. & Kim, M. (2019) Quality-adjusted international price comparisons of mobile telecommunications services. Telecommunications Policy 43, 4, pp. 339–352.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b55ecb3e-7ded-49ce-b8d2-24fe9a10b1dc
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