Study on transmission quality in cellular 4G and 5G networks between 2019–2021: Impact of the COVID-19 pandemic on the level of provided services by operating base transceiver stations

• Autorzy: Falkowski-Gilski Przemysław , Uhl Tadeus

Identyfikatory

DOI

10.17402/555

• Języki publikacji: EN

Abstrakty

EN

The COVID-19 pandemic has significantly limited user mobility, not least among students. Remote learning had a particular impact on resource allocation in relation to using terrestrial cellular networks, especially 4G systems in urban agglomerations. This paper presents the results of a quality evaluation of an outdoor environment, carried out between 2019 and 2021 on the campus of a technical university. Annual studies are conducted using our own custom-built mobile application, installed on 50 mobile devices (i.e., smartphones) running Android OS. This study aims to determine the impact of reduced user mobility on access parameters in mobile networks, that is, both download and upload throughput as well as delay (ping), with a particular focus on serving base transceiver stations (BTSs). This research scenario involves long-term evolution (LTE) compatible user equipment (UE) that operates under four Polish mobile network operators (MNO), which includes roaming connections and the newly launched 5G standard.

Słowa kluczowe

EN

mobile technologies; multimedia transmission; QoN; Quality of Network; QoS; Quality of Service; quality evaluation

• Wydawca: Wydawnictwo Naukowe Akademii Morskiej w Szczecinie
• Czasopismo: Zeszyty Naukowe Akademii Morskiej w Szczecinie
• Rocznik: 2023
• Tom: nr 73 (145)
• Strony: 54--63
• Opis fizyczny: Bibliogr. 45 poz., rys., tab.

Twórcy

autor

Falkowski-Gilski Przemysław

• Gdansk University of Technology, Faculty of Electronics, Telecommunications and Informatics 11/12 Narutowicza St., 80-233 Gdańsk, Poland

• https://orcid.org/0000-0001-8920-6969

autor

Uhl Tadeus

• Maritime University of Szczecin, Faculty of Transport Engineering and Economics 1-2 Wały Chrobrego St., 70-500 Szczecin, Poland

• https://orcid.org/0000-0001-6849-9168

Bibliografia

• 1. Aazam, M., Zeadally, S. & Flushing, E.F. (2021) Task offloading in edge computing for machine learning-based smart healthcare. Computer Networks 191, 108019.
• 2. Adarsh, V., Nekrasov, M., Paul, U., Ermakov, A., Gupta, A., Vigil-Hayes, M., Zegura, E. & Belding, E. (2021) Too late for playback: estimation of video stream quality in rural and urban contexts. In: Proceedings of the Passive and Active Measurement: 22nd International Conference (PAM) Virtual Event, Online, 29 March – 1 April 1 2021, pp. 141–157.
• 3. Adil, M. & Khan, M.K. (2021) Emerging IoT applications in sustainable smart cities for Covid-19: Network security and data preservation challenges with future directions. Sustainable Cities and Society 75, 103311.
• 4. Anand, D., Togou, M.A. & Muntean, G.M. (2022a) A machine learning solution for video delivery to mitigate co-tier interference in 5G HetNets. IEEE Transactions on Multimedia (early access), doi: 10.1109/TMM.2022.3187607.
• 5. Anand, D., Togou, M.A. & Muntean, G.M. (2022b) A machine learning solution to mitigate co-tier interference and improve QoE for video delivery in 5G HetNets. IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), Bilbao, Spain, 15–17 June 2022, doi: 10.1109/BMSB55706.2022.9828785.
• 6. Balmuri, K.R., Konda, S., Lai, W.C., Divakarachari, P.B., Gowda, K.M.V. & Kivudujogappa Lingappa, H. (2022) A long short-term memory network-based radio resource management for 5G network. Future Internet 14, 184.
• 7. Barczyk, M. & Chydziński, A. (2022) AQM based on the queue length: A real-network study. PLoS ONE 17(2), e0263407, doi: 10.1371/journal.pone.0263407.
• 8. Biernacki, A. (2022) Improving streaming video with deep learning-based network throughput prediction. Applied Sciences 12(20), 10274.
• 9. Biernacki, A. & Tutschku, K. (2014) Performance of HTTP video streaming under different network conditions. Multimedia Tools and Applications 72(2), pp. 1143–1166.
• 10. Carofiglio, G., Grassi, G., Loparco, E., Muscariello, L., Papalini, M. & Samain, J. (2021) Characterizing the relationship between application QoE and network QoS for real-time services. In: Proceedings of the ACM SIGCOMM 2021 Workshop on Network-Application Integration (NAI), Virtual Event, Online, 23–27 August 2021, pp. 20–25.
• 11. Cheng, Q., Li, B. & Zhou, Y. (2021) Research on evaluation system of classroom teaching quality in colleges and universities based on 5G environment. In: Proceedings of the 2021 1st International Conference on Control and Intelligent Robotics (ICCIR), Guangzhou, China, 18–20 June 2021, ACM, pp. 74–85, doi: 10.1145/3473714.3473728.
• 12. Chydziński, A. & Samociuk, D. (2019) Burst ratio in a single-server queue. Telecommunication Systems 70, pp. 263– 276.
• 13. Cichoń, K., Kliks, A. & Bogucka, H. (2016) Energy-efficient cooperative spectrum sensing: A survey. IEEE Communications Surveys &Tutorials 18(3), pp. 1861–1886.
• 14. El-Saleh, A.A., Alhammadi, A., Shayea, I., Alsharif, N., Alzahrani, N.M., Khalaf, O.I. & Aldhyani, T.H. (2022) Measuring and assessing performance of mobile broadband networks and future 5G trends. Sustainability 14(2), 829.
• 15. Elsayed, M.S., Le-Khac, N.A. & Jurcut, A.D. (2021) Dealing with COVID-19 network traffic spikes [cybercrime and forensics]. IEEE Security & Privacy 19(1), pp. 90–94.
• 16. Falkowski-Gilski, P. (2020) On the consumption of multimedia content using mobile devices: A year to year user case study. Archives of Acoustics 45(2), pp. 321–328.
• 17. 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.
• 18. Falkowski-Gilski, P. & Uhl, T. (2022) Cellular network quality evaluation at a university campus on the eve of 5G. Scientific Journals of the Maritime University of Szczecin, Zeszyty Naukowe Akademii Morskiej w Szczecinie 70 (142), pp. 73–80.
• 19. Fang, Z., Yang, Y., Yang, G., Xian, Y., Zhang, F. & Zhang, D. (2021) CellSense: Human mobility recovery via cellular network data enhancement. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5(3), 100, doi: 10.1145/3478087.
• 20. Favale, T., Soro, F., Trevisan, M., Drago, I. & Mellia, M. (2020) Campus traffic and e-learning during COVID-19 pandemic. Computer Networks 176, 107290.
• 21. GUT (2023) Gdansk University of Technology. Campus of GUT. [Online] Available: https://campus.pg.edu.pl [Accessed: January 2023].
• 22. Gutierrez, J., Perez, P., Orduna, M., Singla, A., Cortes, C., Mazumdar, P., Viola, I., Brunnstrom, K., Battisti, F., Cieplińska, N., Juszka, D., Janowski, L., Leszczuk, M., Adeyemi-Ejeye, A., Hu, Y., Chen, Z., Van Wallendael, G., Lambert, P., Diaz, C., Hed-lund, J., Hamsis, O., Fremerey, S., Hofmeyer, F., Raake, A., Cesar, P., Carli, M. & Garcia, N. (2022) Subjective evaluation of visual quality and simulator sickness of short 360 degrees videos: ITU-T Rec. P.919. IEEE Transactions on Multimedia 24, pp. 3087–3100.
• 23. Hernández-Orallo, E. & Armero-Martínez, A. (2021) How human mobility models can help to deal with COVID-19. Electronics 10(1), 33.
• 24. Khatib, E.J., Perles Roselló, M.J., Miranda-Páez, J., Giralt, V. & Barco, R. (2021) Mass tracking in cellular networks for the COVID-19 pandemic monitoring. Sensors 21(10), 3424.
• 25. Korchani, B. & Sethom, K. (2022) Machine learning for student QoE prediction in mobile learning during COVID-19. In: Barolli, L., Hussain, F., Enokido, T. (Eds) Advanced Information Networking and Applications. Springer, Cham, pp. 14–22.
• 26. Laso, S., Berrocal, J., Fernández, P., Ruiz-Cortés, A. & Murillo, J.M. (2022) Perses: A framework for the continuous evaluation of the QoS of distributed mobile applications. Pervasive and Mobile Computing 84, 101627.
• 27. Lin, X., Wu, W., Zhu, Y., Qiu, T. & Mi, Z. (2016) SARS: A novel QoE based service-aware resource scheduling scheme in wireless network. IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB), Nanjing, China, 16–19 October 2016, doi: 10.1109/ICUWB. 2016.7790527.
• 28. Lord, F.M. & Novick, M.R. (2008) Statistical theories of mental test scores. Information Age Published: Charlotte.
• 29. Lutu, A., Perino, D., Bagnulo, M., Frias-Martinez, E. & Khangosstar, J.A (2020) Characterization of the COVID-19 pandemic impact on a mobile network operator traffic. In: Proceedings of the 2020 ACM Internet Measurement Conference (IMC), Virtual Event, USA, 27–29 October 2020, ACM.
• 30. Mongay Batalla, J., Krawiec, P., Bęben, A., Wiśniewski, P. & Chydziński, A. (2016) Adaptive video streaming: rate and buffer on the track of minimum rebuffering. IEEE Journal on Selected Areas in Communications 34(8), pp. 2154– 2167.
• 31. Mongay Batalla, J., Moshin, M., Mavromoustakis, C.X., Wesołowski, K., Mastorakis, G. & Krzykowska-Piotrowska, K. (2022) On deploying the Internet of energy with 5G open RAN technology including beamforming mechanism. Energies 15(7), 2429.
• 32. Nightingale, J., Salva-Garcia, P., Calero, J.M.A. & Wang, Q. (2018) 5G-QoE: QoE modelling for ultra-HD video streaming in 5G networks. IEEE Transactions on Broadcasting 64(2), pp. 621–634.
• 33. Nowicki, K. & Uhl, T. (2017) QoS/QoE in the heterogeneous Internet of things (IoT), In: Mongay Batalla, J., Mastorakis, G., Mavromoustakis, C.X., Pallis, E. (Eds) Beyond the Internet of Things. Springer, pp. 165–196.
• 34. NSC (2023) Nextragen Solutions Company. [Online] Available: https://nextragen-solutions.de [Accessed: January 2023].
• 35. Pal, D., Vanijja, V. & Patra, S. (2020) Online learning during COVID-19: Students’ perception of multimedia quality. In: Proceedings of the 11th International Conference on Advances in Information Technology (IAIT), Bangkok, Thailand, 1–3 July 2020, ACM.
• 36. Perumal, K., Mohan, S., Frnda, J. & Divakarachari, P.B. (2022) Dynamic resource provisioning and secured file sharing using virtualization in cloud azure. Journal of Cloud Computing 11, 46.
• 37. Ramachandra, M.N., Srinivasa Rao, M., Lai, W.C., Parameshachari, B.D. Ananda Babu, J. & Hemalatha, K.L. (2022) An efficient and secure big data storage in cloud environment by using triple data encryption standard. Big Data and Cognitive Computing 6(4), 101.
• 38. 8. Rananga, N. & Venter, H.S. (2020) Mobile cloud computing adoption model as a feasible response to countries’ lockdown as a result of the COVID-19 outbreak and beyond. IEEE Conference on e-Learning, e-Management and e-Services (IC3e), Kota Kinabalu, Malaysia, 17–19 November 2020, doi: 10.1109/IC3e50159.2020.9288402.
• 39. RSC (2023) Rodhe & Schwarz Company. [Online] Available: https://www.rohde-schwarz.com/ch/loesungen/broadcast-and-media/overview/broadcast-und-medientechnik-uebersicht_229836.html [Accessed: January 2023].
• 40. Rybka, P., Bąk, T., Sobel, P. & Grzechca, D. (2022) Investigation of the impact of damaged smartphone sensors’ readings on the quality of behavioral biometric models. Sensors 22(24), 9580.
• 41. SPABC (2023) Systemics-PAB Company. [Online] Available: https://www.syspab.eu [Accessed: January 2023].
• 42. Syauqi, K., Munadi, S. & Triyono, M.B. (2020) Students’ perceptions toward vocational education on online learning during the COVID-19 pandemic. International Journal of Evaluation and Research in Education 9(4), pp. 881–886.
• 43. Tang, B., Guo, S., Yeboah, M., Wang, Z. & Cheng, S. (2021) Quality evaluation of online courses during COVID-19 pandemic based on integrated FCE-AHP method. Journal of Intelligent & Fuzzy Systems 41(1), pp. 1487– 1498.
• 44. Weichbroth, P. (2022) An empirical study on the impact of gender on mobile applications usability. IEEE Access 10, pp. 119419–119436, doi: 10.1109/ACCESS.2022.3219421.
• 45. Yassine, A. & Hossain, M.S. (2022) COVID-19 networking demand: An auction-based mechanism for automated selection of edge computing services. IEEE Transactions on Network Science and Engineering 9(1), pp. 308–318.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).