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The Method of Redistributing Traffic in Mobile Network

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Data-Centric Business and Applications

Abstract

Cellular network generates a lot of signaling data. A large part of signaling data is generated to handle the mobility of subscribers and contains location information that can be used to fundamentally change our understanding of mobility principle. However, location data available from standard interfaces in cellular networks is very an important research question is how this data can be processed in order to efficiently use it for traffic state estimation and traffic planning. The design of the mobile operator’s network is carried out by the method of frequency spatial planning. It is believed that the solution to this problem provides the required indicators of electromagnetic compatibility of network elements, and as a result, performance of the network. Ideally, these findings should be replicated in a study where uniformity of traffic over network elements is relegated to the background. Results provide a basis for affects both throughput and quality of service. In this paper, it is proposed to use the sector analysis method for optimizing the load distribution between base stations when predicting the coverage areas of base stations, in addition to using the frequency-spatial planning method, when forecasting service areas of base stations. The technology of cellular systems is changing at such a speed that 4G networks have not yet had time to fully deploy, as 5G is already being introduced. The fourth generation is characterized by LTE-advanced technology, which implies an intelligent network with self-training and partial adjustment of its parameters. The distribution functions of the radio resource of the cellular communication network of this standard lie at the base stations. However, clear control algorithms for such networks have not yet been developed. As part of situationally adaptive planning of radio resources in radio communication systems, a method is proposed for determining the optimal coverage areas of base stations depending on the distribution of subscribers according to billing data. To this end, in addition to the statistics for base stations for servicing the load, enrich it with billing system data.

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References

  1. Romanov O, Mankivskyi V (2019) Optimal traffic distribution based on the sectoral model of loading network elements. In: IEEE international scientific-practical conference problems of infocommunications, science and technology (PIC S&T), pp 683–688

    Google Scholar 

  2. Gaydamak Y, Zaripova E, Samuylov K (2008) Cellular mobile call service models. Russian University of Friendship, Moscow Russia

    Google Scholar 

  3. Fuchs C, Aschenbruck N, Martini P, Wieneke M (2011) Indoor tracking for mission critical scenarios: a survey. Pervasive Mob Comput 7(1):1–15

    Article  Google Scholar 

  4. Cuevas A, Moreno JI, Einsiedler H (2006) IMS service platform: a solution for next-generation network operators to be more than bit pipes. IEEE Commun Mag 75–81

    Google Scholar 

  5. Ho W-C, Tung L-P, Chang T-S, Feng K-T (2013) Enhanced component carrier selection and power allocation in LTE-advanced downlink systems. In: Wireless communications and networking conference (WCNC), IEEE, pp 574–579

    Google Scholar 

  6. Romanov O, Dong TT, Nesterenko M (2020) The possibilities for deployment eco-friendly indoor wireless networks based on LiFi technology. In: 8-th International conference on applied innovations in IT, (ICAIIT)

    Google Scholar 

  7. Skulysh M, Romanov O (2018) The structure of a mobile provider network with network functions virtualization. In: TCSET 2018: 14-th international conference on advanced trends in radioelectronics, telecommunications and computer engineering, 20–24 February 2018: conference proceedings. Lviv–Slavske, pp 1032–1034

    Google Scholar 

  8. Romanov O, Nesterenko M, Veres L (2017) IMS: model and calculation method of telecommunication network’s capacity. In: Proceedings of the 2017 international conference on information and telecommunication technologies and radio electronics (UkrMiCo) 11–15 Sept 2017, Odessa, Ukraine. IEEE Conference Publications, pp 1–4

    Google Scholar 

  9. Popoola S, Oseni O (2014) Empirical path loss models for GSM network deployment in Makurdi, Nigeria. Int J Sci 3(6):85–94

    Google Scholar 

  10. Skulysh M, Klimovych O (2015) Approach to virtualization of evolved packet core network functions. In: The 13th international conference experience of designing and application of CAD systems in microelectronics (CADSM). IEEE, pp 193–195

    Google Scholar 

  11. Globa L, Skulysh M, Romanov O, Nesterenko M (2018) Quality control for mobile communication management services in hybrid environment. In: The international conference on information and telecommunication technologies and radio electronics. Springer, Cham, pp 76–100

    Google Scholar 

  12. Romanov O, Nesterenko M, Mankivskyi V (2016) Application of the regression model of the coefficient of use of channels for forming the plan of load distribution in the network. In: Bulletin of NTUU “KPI”. Radio engineering series, radio apparatus construction, No 67, pp 34–42

    Google Scholar 

  13. Degollado-Rea A, Vidal-Beltrán S, López-Bonilla J, Thapa GB (2015) Okumura-Hata, walfish-ikegami and 3GPP propagation models in urban environments for UMTS networks. SciTech J Sci Technol 4(1):70–78

    Google Scholar 

  14. Tahcfulloh S, Riskayadi E (2015) Optimized suitable propagation model for GSM. Telkomnika Indonesian J Electr Eng 14(1):154–162

    Google Scholar 

  15. Ilchenko M, Uryvsky L, Moshynska A (2017) Developing of telecommunication strategies based on the scenarios of the information community. Cybern Syst Anal. 53(6):905–913

    Google Scholar 

  16. Skulysh M, Romanov O (2018) The structure of a mobile provider network with network functions virtualization. In: 14th International conference on advanced trends in radioelecrtronics, telecommunications and computer engineering (TCSET). IEEE, 1032–1034

    Google Scholar 

  17. Romanov O, Hordashnyk Y, Dong T (2017) Method for calculating the energy loss of a light signal in a telecommunication Li-Fi system. In: Proceedings of the 2017 international conference on information and telecommunication technologies and radio electronics (UkrMiCo), 11–15 Sept 2017, Odessa, Ukraine. IEEE Conference Publications

    Google Scholar 

  18. Daradkeh YI, Kirichenko L, Radivilova T (2018) Development of QoS methods in the information networks with fractal traffic. Int J Electron Telecommun 64(1):27–32. https://doi.org/10.24425/118142

    Article  Google Scholar 

  19. Ageyev D et al (2019) Infocommunication networks design with self-similar traffic. In: IEEE 15th international conference on the experience of designing and application of CAD systems (CADSM). IEEE, pp 24–27. https://doi.org/10.1109/cadsm.2019.8779314

  20. Kryvinska N (2004) Intelligent network analysis by closed queuing models. Telecommun Syst 27:85–98. https://doi.org/10.1023/B:TELS.0000032945.92937.8f

    Article  Google Scholar 

  21. Skulysh MA, Romanov OI, Globa LS, Husyeva II (2019) Managing the process of servicing hybrid telecommunications services. Quality control and interaction procedure of service subsystems. In: Advances in intelligent systems and computing, vol 889, pp 244–256

    Google Scholar 

  22. Kurdecha VV, Zingaeva NA (2011) Optimal reconfigurable base stations (R-BS) architecture and requirements to R-BS. In: 21st international crimean conference “microwave and telecommunication technology”, Sevastopol, pp 465–466

    Google Scholar 

  23. Moshynska A, Osypchuk S, Pieshkin A, Shmihel B (2018) The effect of the features of signalcode constructions forming on indicators of functionality and reliability of communication systems based on the 802.11 N/AC standards. J Sci Europe 2(26):38–47. Praha, Czech Republic. (ISSN 3162-2364)

    Google Scholar 

  24. Ageyev D, Qasim N (2015) LTE EPS network with self-similar traffic modeling for performance analysis. In: Proceedings of the 2015 second international scientific-practical conference problems of infocommunications science and technology (PIC S&T). IEEE, Kharkov, Ukraine, pp 275–277. https://doi.org/10.1109/infocommst.2015.7357335

  25. Kryvinska N (2008) An analytical approach for the modeling of real-time services over IP network. Math Comput Simul 79(4):980–990. https://doi.org/10.1016/j.matcom.2008.02.016

    Article  MathSciNet  MATH  Google Scholar 

  26. Barabash O, Lukova-Chuiko N, Sobchuk V, Musienko A (2018) Application of petri networks for support of functional stability of information systems. In: IEEE 1st international conference on system analysis and intelligent computing, SAIC 2018—Proceedings. IEEE, Kyiv, Ukraine, pp 1–4. https://doi.org/10.1109/SAIC.2018.8516747

  27. Ghosh A, Ratasuk R, Mondal B, Mangalvedhe N, Thomas T (2010) LTE-advanced: next-generation wireless broadband technology. IEEE Wirel Commun 17(3):10–22

    Article  Google Scholar 

  28. Romanov O, Fediushyna D, Dong T (2018) Model and method of Li-Fi network calculation with multipath light signals. In: International conference on information and telecommunication technologies and radio electronics (UkrMiCo), 10–14 Sept 2018

    Google Scholar 

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Romanov, O., Nesterenko, M., Mankivskyi, V. (2021). The Method of Redistributing Traffic in Mobile Network. In: Ageyev, D., Radivilova, T., Kryvinska, N. (eds) Data-Centric Business and Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-030-71892-3_7

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