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Automation and Remote Control

, Volume 80, Issue 11, pp 2017–2032 | Cite as

Efficient Algorithm for Evaluating the Required Volume of Resource in Wireless Communication Systems under Joint Servicing of Heterogeneous Traffic for the Internet of Things

  • S. N. StepanovEmail author
  • M. S. StepanovEmail author
Stochastic Systems
  • 11 Downloads

Abstract

We construct and study a mathematical model of the distribution of the resource for transmitting information for an isolated cell of an LTE standard mobile network with the joint servicing of heterogeneous traffic from Internet of Things devices. The model considers an arbitrary number of streams of multimedia traffic, which differ in the intensity of the arrival of communication sessions, the size of the resource used to service one session, the time of resource occupation, and the probability of a session being allowed to transmit the information stream. We determine quality of service characteristics for incoming sessions and construct an effective algorithm for estimating the amount of resource required to service given traffic flows with required quality. Efficiency of the algorithm is achieved as a result of the implementation of recursion with respect to the available resource and the use of normalized probabilities of model states during calculations. The algorithm is computationally stable and allows to solve the resource estimation problem many times faster than traditional approaches based on calculating the probabilities of all states for each resource value and their subsequent normalization. We give numerical examples illustrating the implementation features of developed computational procedures.

Keywords

Internet of Things multiservice traffic Markov models system of equilibrium equations recursive algorithms transmission resource planning 

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Notes

Acknowledgments

This work was partially supported by the Russian Foundation for Basic Research, project no. 16-29-09497ofi-m.

References

  1. 1.
    Study on Provision of Low-Cost Machine-Type Communications (MTC) User Equipments (UEs) Based on LTE, 3GPP Technical Report (TR) 36.888/r12, 2013.Google Scholar
  2. 2.
    3GPP. Standardization of NB-IOT Completed. www.3gpp.org/news-events/3gpp-news/1785-nb iot complete, June 2016.
  3. 3.
    3GPP Technical Report (TR) 36.888/r12, Study on Provision of Low-Cost Machine-Type Communications (MTC) User Equipments (UEs) Based on LTE, 2013. http://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=2578Google Scholar
  4. 4.
    Rico-Alvarino, A., Vajapeyam, M., Xu, H., Wang, X., Blankenship, Y., Bern, J., Tirronen, T., and Yavuz, E., An Overview of 3GPP Enhancements on Machine to Machine Communications, IEEE Commun. Mag., 2016, vol. 54, no. 6, pp. 14–21.CrossRefGoogle Scholar
  5. 5.
    In December 2017, State Committee on Radiofrequencies Made a decision to Allot Frequencies for NBIoT Systems. https://digital.gov.ru/ru/documents/5875/
  6. 6.
    Begishev, V., Petrov, V., Samuylov, A., Moltchanov, D., Andreev, S., Koucheryavy, Y., and Samouylov, K., Resource Allocation and Sharing for Heterogeneous Data Collection over Conventional 3GPP LTE and Emerging NB-IoT Technologies, Comput. Communicat., 2018, vol. 120, no. 2, pp. 93–101.CrossRefGoogle Scholar
  7. 7.
    Shorgin, S., Samouylov, K., Gaidamaka, Y., Chukarin, A., Buturlin, I., and Begishev, V., Modeling Radio Resource Allocation Scheme with Fixed Transmission Zones for Multiservice M2M Communications in Wireless IoT Infrastructure, in Lecture Notes Comput. Sci., Cham: Springer, 2015, vol. 9012, pp. 473–483.CrossRefGoogle Scholar
  8. 8.
    Begishev, V.O., Samuilov, A.K., Molchanov, D.A., and Samuilov, K.E., A Strategy for Radio Resource Distribution in Heterogeneous Networks with Narrow-Band IoT Traffic, Sist. Sredstva Informat., 2017, no. 4, vol. 27, pp. 64–79.Google Scholar
  9. 9.
    Broadband Network Traffic. Performance Evaluation and Design of Broadband Multiservice Networks. Final Report of Action COST 242, in Lecture Notes in Computer Science, Roberts, J., Ed., Berlin: Springer, 1996.Google Scholar
  10. 10.
    Stepanov, S.N., Teoriya teletrafika: kontseptsii, modeli, prilozheniya (Teletraffic Theory: Concepts, Models, Applications), Moscow: Goryachaya Liniya-Telekom, 2015.Google Scholar
  11. 11.
    Stepanov, S.N., Model of Joint Servicing of Real-Time Service Traffic and Data Traffic. I, Autom. Remote Control, 2011, vol. 72, no. 4, pp. 787–797.MathSciNetCrossRefGoogle Scholar
  12. 12.
    Stepanov, S.N., Model of Joint Servicing of Real-Time Service Traffic and Data Traffic. II, Autom. Remote Control, 2011, vol. 72, no. 5, pp. 1028–1035.MathSciNetCrossRefGoogle Scholar
  13. 13.
    Stepanov, S.N. and Stepanov, M.S., Planning Transmission Resource at Joint Servicing of the Multiservice Real Time and Elastic Data Traffics, Autom. Remote Control, 2017, vol. 78, no. 11, pp. 2004–2015.MathSciNetCrossRefGoogle Scholar
  14. 14.
    Stepanov, S.N. and Stepanov, M.S., Planning the Resource of Information Transmission for Connection Lines of Multiservice Hierarchical Access Networks, Autom. Remote Control, 2018, vol. 79, no. 8, pp. 1422–1433.MathSciNetCrossRefGoogle Scholar
  15. 15.
    Stepanov, S.N., Solution of Simultaneous Large-Scale Equilibrium Equations, Autom. Remote Control, 1989, vol. 50, no. 5, part 2, pp. 647–655.zbMATHGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  1. 1.Moscow Technical University of Communications and Computer ScienceMoscowRussia

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