IDEAS home Printed from https://ideas.repec.org/a/spr/telsys/v86y2024i4d10.1007_s11235-024-01155-0.html
   My bibliography  Save this article

Dynamical modelling and cost optimization of a 5G base station for energy conservation using feedback retrial queue with sleeping strategy

Author

Listed:
  • R. Harini

    (Vellore Institute of Technology)

  • K. Indhira

    (Vellore Institute of Technology)

Abstract

Dense network deployment is now being evaluated as one of the viable solutions to meet the capacity and connectivity requirements of the fifth-generation (5G) cellular system. The goal of 5G cellular networks is to offer clients with faster download speeds, lower latency, more dependability, broader network capacities, more accessibility, and a seamless client experience. However, one of the many obstacles that will need to be overcome in the 5G era is the issue of energy usage. For energy efficiency in 5G cellular networks, researchers have been studying at the sleeping strategy of base stations. In this regard, this study models a 5G BS as an $$M^{[X]}/G/1$$ M [ X ] / G / 1 feedback retrial queue with a sleeping strategy to reduce average power consumption and conserve power in 5G mobile networks. The probability-generating functions and steady-state probabilities for various BS states were computed employing the supplementary variable approach. In addition, an extensive palette of performance metrics have been determined. Then, with the aid of graphs and tables, the resulting metrics are conceptualized and verified. Further, this research is accelerated in order to bring about the best possible (optimal) cost for the system by adopting a range of optimization approaches namely particle swarm optimization, artificial bee colony and genetic algorithm.

Suggested Citation

  • R. Harini & K. Indhira, 2024. "Dynamical modelling and cost optimization of a 5G base station for energy conservation using feedback retrial queue with sleeping strategy," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 86(4), pages 661-690, August.
    • Handle: RePEc:spr:telsys:v:86:y:2024:i:4:d:10.1007_s11235-024-01155-0
    DOI: 10.1007/s11235-024-01155-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11235-024-01155-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11235-024-01155-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Shweta Upadhyaya, 2020. "Cost optimisation of a discrete-time retrial queue with Bernoulli feedback and starting failure," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 36(2), pages 165-196.
    2. Priyanka Kalita & Dharmaraja Selvamuthu, 2023. "Stochastic modelling of sleeping strategy in 5G base station for energy efficiency," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 83(2), pages 115-133, June.
    3. G. Ayyappan & M. Nirmala, 2021. "Analysis of customer's impatience on bulk service queueing system with unreliable server, setup time and two types of multiple vacations," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 38(2), pages 198-222.
    4. V. Deepa & M. Haridass & Dharmaraja Selvamuthu & Priyanka Kalita, 2023. "Analysis of energy efficiency of small cell base station in 4G/5G networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 82(3), pages 381-401, March.
    5. Kumar, Anshul & Jain, Madhu, 2023. "Cost Optimization of an Unreliable server queue with two stage service process under hybrid vacation policy," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 204(C), pages 259-281.
    6. A. G. Pakes, 1969. "Some Conditions for Ergodicity and Recurrence of Markov Chains," Operations Research, INFORMS, vol. 17(6), pages 1058-1061, December.
    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. R. Harini & K. Indhira, 2024. "Meta heuristic optimization of a batch arrival retrial queue with optional re-service and M-optional vacations," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(9), pages 4252-4282, September.
    2. Dieter Fiems & Koen Turck, 2025. "The delay-energy trade-off for advanced sleep modes in radio access networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 88(1), pages 1-8, March.
    3. Chahal, Parmeet Kaur & Kumar, Kamlesh & Soodan, Bhavneet Singh, 2024. "Grey wolf algorithm for cost optimization of cloud computing repairable system with N-policy, discouragement and two-level Bernoulli feedback," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 225(C), pages 545-569.
    4. Jeongsim Kim & Bara Kim, 2016. "A survey of retrial queueing systems," Annals of Operations Research, Springer, vol. 247(1), pages 3-36, December.
    5. Sunggon Kim & Jongwoo Kim & Eui Lee, 2006. "Stationary distribution of queue length in G / M / 1 queue with two-stage service policy," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 64(3), pages 467-480, December.
    6. Baumann, Hendrik & Sandmann, Werner, 2014. "On finite long run costs and rewards in infinite Markov chains," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 41-46.
    7. Falin, G.I., 2010. "A single-server batch arrival queue with returning customers," European Journal of Operational Research, Elsevier, vol. 201(3), pages 786-790, March.
    8. Gorbunova, A.V. & Lebedev, A.V., 2023. "Nonlinear approximation of characteristics of a fork–join queueing system with Pareto service as a model of parallel structure of data processing," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 214(C), pages 409-428.
    9. Shweta Upadhyaya, 2020. "Investigating a general service retrial queue with damaging and licensed units: an application in local area networks," OPSEARCH, Springer;Operational Research Society of India, vol. 57(3), pages 716-745, September.
    10. T. Deepak, 2015. "On a retrial queueing model with single/batch service and search of customers from the orbit," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 493-520, July.
    11. Ioannis Dimitriou, 2013. "A preemptive resume priority retrial queue with state dependent arrivals, unreliable server and negative customers," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(3), pages 542-571, October.
    12. Xiang, Shizhe & Xu, Da & Wang, Pengda & Bai, Ziyi & Zeng, Lingxiong, 2024. "Optimal expansion planning of 5G and distribution systems considering source-network-load-storage coordination," Applied Energy, Elsevier, vol. 366(C).
    13. V. Jailaxmi & R. Arumuganathan & M. Senthil Kumar, 2017. "Performance analysis of an M/G/1 retrial queue with general retrial time, modified M-vacations and collision," Operational Research, Springer, vol. 17(2), pages 649-667, July.
    14. Kahraman, Aykut & Gosavi, Abhijit, 2011. "On the distribution of the number stranded in bulk-arrival, bulk-service queues of the M/G/1 form," European Journal of Operational Research, Elsevier, vol. 212(2), pages 352-360, July.
    15. I. Atencia & G. Bouza & P. Moreno, 2008. "An M [X] /G/1 retrial queue with server breakdowns and constant rate of repeated attempts," Annals of Operations Research, Springer, vol. 157(1), pages 225-243, January.
    16. P. Rajadurai & V. M. Chandrasekaran & M. C. Saravanarajan, 2016. "Analysis of an M[X]/G/1 unreliable retrial G-queue with orbital search and feedback under Bernoulli vacation schedule," OPSEARCH, Springer;Operational Research Society of India, vol. 53(1), pages 197-223, March.

    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:spr:telsys:v:86:y:2024:i:4:d:10.1007_s11235-024-01155-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.