IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i5p800-d358137.html
   My bibliography  Save this article

Queuing System with Unreliable Servers and Inhomogeneous Intensities for Analyzing the Impact of Non-Stationarity to Performance Measures of Wireless Network under Licensed Shared Access

Author

Listed:
  • Ekaterina Markova

    (Applied Probability and Informatics Department, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya St, 117198 Moscow, Russia)

  • Yacov Satin

    (Department of Mathematics, Vologda State University, Lenina 15, 160000 Vologda, Russia)

  • Irina Kochetkova

    (Applied Probability and Informatics Department, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya St, 117198 Moscow, Russia
    Institute of Informatics Problems, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Vavilova 44-2, 119333 Moscow, Russia)

  • Alexander Zeifman

    (Department of Mathematics, Vologda State University, Lenina 15, 160000 Vologda, Russia
    Institute of Informatics Problems, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Vavilova 44-2, 119333 Moscow, Russia
    Vologda Research Center RAS, Gorky Street 56A, 160014 Vologda, Russia)

  • Anna Sinitcina

    (Department of Mathematics, Vologda State University, Lenina 15, 160000 Vologda, Russia)

Abstract

Given the limited frequency band resources and increasing volume of data traffic in modern multiservice networks, finding new and more efficient radio resource management (RRM) mechanisms is becoming indispensable. One of the implemented technologies to solve this problem is the licensed shared access (LSA) technology. LSA allows the spectrum that has been licensed to an owner, who has absolute priority on its utilization, to be used by other participants (i.e., tenants). Owner priority impacts negatively on the quality of service (QoS) by reducing the data bit rate and interrupting user services. In this paper, we propose a wireless multiservice network scheme model described as a queuing system with unreliable servers and a finite buffer within the LSA framework. The aim of this work is to analyze main system performance measures: blocking probability, average number of requests in queue, and average queue length depending on LSA frequencies’ availability.

Suggested Citation

  • Ekaterina Markova & Yacov Satin & Irina Kochetkova & Alexander Zeifman & Anna Sinitcina, 2020. "Queuing System with Unreliable Servers and Inhomogeneous Intensities for Analyzing the Impact of Non-Stationarity to Performance Measures of Wireless Network under Licensed Shared Access," Mathematics, MDPI, vol. 8(5), pages 1-13, May.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:5:p:800-:d:358137
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/5/800/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/5/800/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zeifman, A. & Satin, Y. & Kiseleva, K. & Korolev, V. & Panfilova, T., 2019. "On limiting characteristics for a non-stationary two-processor heterogeneous system," Applied Mathematics and Computation, Elsevier, vol. 351(C), pages 48-65.
    2. Schwarz, Justus Arne & Selinka, Gregor & Stolletz, Raik, 2016. "Performance analysis of time-dependent queueing systems: Survey and classification," Omega, Elsevier, vol. 63(C), pages 170-189.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yacov Satin & Alexander Zeifman & Anastasia Kryukova, 2019. "On the Rate of Convergence and Limiting Characteristics for a Nonstationary Queueing Model," Mathematics, MDPI, vol. 7(8), pages 1-11, July.
    2. Pei, Zhi & Dai, Xu & Yuan, Yilun & Du, Rui & Liu, Changchun, 2021. "Managing price and fleet size for courier service with shared drones," Omega, Elsevier, vol. 104(C).
    3. Yacov Satin & Rostislav Razumchik & Ivan Kovalev & Alexander Zeifman, 2023. "Ergodicity and Related Bounds for One Particular Class of Markovian Time—Varying Queues with Heterogeneous Servers and Customer’s Impatience," Mathematics, MDPI, vol. 11(9), pages 1-15, April.
    4. Andersen, Anders Reenberg & Nielsen, Bo Friis & Reinhardt, Line Blander & Stidsen, Thomas Riis, 2019. "Staff optimization for time-dependent acute patient flow," European Journal of Operational Research, Elsevier, vol. 272(1), pages 94-105.
    5. Ad Ridder, 2022. "Rare-event analysis and simulation of queues with time-varying rates," Queueing Systems: Theory and Applications, Springer, vol. 100(3), pages 545-547, April.
    6. Ansari, Sardar & Yoon, Soovin & Albert, Laura A., 2017. "An approximate hypercube model for public service systems with co-located servers and multiple response," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 143-157.
    7. Yacov Satin & Alexander Zeifman & Alexander Sipin & Sherif I. Ammar & Janos Sztrik, 2020. "On Probability Characteristics for a Class of Queueing Models with Impatient Customers," Mathematics, MDPI, vol. 8(4), pages 1-15, April.
    8. Hu, Lu & Zhao, Bin & Zhu, Juanxiu & Jiang, Yangsheng, 2019. "Two time-varying and state-dependent fluid queuing models for traffic circulation systems," European Journal of Operational Research, Elsevier, vol. 275(3), pages 997-1019.
    9. Giorno, Virginia & Nobile, Amelia G., 2022. "On some integral equations for the evaluation of first-passage-time densities of time-inhomogeneous birth-death processes," Applied Mathematics and Computation, Elsevier, vol. 422(C).
    10. Tsiligianni, Christiana & Tsiligiannis, Aristeides & Tsiliyannis, Christos, 2023. "A stochastic inventory model of COVID-19 and robust, real-time identification of carriers at large and infection rate via asymptotic laws," European Journal of Operational Research, Elsevier, vol. 304(1), pages 42-56.
    11. Zeifman, A. & Satin, Y. & Kiseleva, K. & Korolev, V. & Panfilova, T., 2019. "On limiting characteristics for a non-stationary two-processor heterogeneous system," Applied Mathematics and Computation, Elsevier, vol. 351(C), pages 48-65.
    12. Mahes, Roshan & Mandjes, Michel & Boon, Marko & Taylor, Peter, 2024. "Adaptive scheduling in service systems: A Dynamic programming approach," European Journal of Operational Research, Elsevier, vol. 312(2), pages 605-626.
    13. Alexander Zeifman & Yacov Satin & Ivan Kovalev & Rostislav Razumchik & Victor Korolev, 2020. "Facilitating Numerical Solutions of Inhomogeneous Continuous Time Markov Chains Using Ergodicity Bounds Obtained with Logarithmic Norm Method," Mathematics, MDPI, vol. 9(1), pages 1-20, December.
    14. Li, Dongmin & Hu, Qingpei & Wang, Lujia & Yu, Dan, 2019. "Statistical inference for Mt/G/Infinity queueing systems under incomplete observations," European Journal of Operational Research, Elsevier, vol. 279(3), pages 882-901.
    15. Raik Stolletz, 2022. "Optimization of time-dependent queueing systems," Queueing Systems: Theory and Applications, Springer, vol. 100(3), pages 481-483, April.
    16. Zeifman, A.I. & Satin, Y.A. & Kiseleva, K.M., 2020. "On obtaining sharp bounds of the rate of convergence for a class of continuous-time Markov chains," Statistics & Probability Letters, Elsevier, vol. 161(C).
    17. Zeifman, A.I. & Razumchik, R.V. & Satin, Y.A. & Kovalev, I.A., 2021. "Ergodicity bounds for the Markovian queue with time-varying transition intensities, batch arrivals and one queue skipping policy," Applied Mathematics and Computation, Elsevier, vol. 395(C).
    18. Narayanan C. Viswanath, 2022. "Transient study of Markov models with time-dependent transition rates," Operational Research, Springer, vol. 22(3), pages 2209-2243, July.
    19. Alexander Zeifman & Victor Korolev & Yacov Satin, 2020. "Two Approaches to the Construction of Perturbation Bounds for Continuous-Time Markov Chains," Mathematics, MDPI, vol. 8(2), pages 1-25, February.
    20. Vijayalakshmi Chetlapalli & K. S. S. Iyer & Himanshu Agrawal, 2020. "Modelling time-dependent aggregate traffic in 5G networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 73(4), pages 557-575, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:8:y:2020:i:5:p:800-:d:358137. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.