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Performance evaluation and system optimization of Green cognitive radio networks with a multiple-sleep mode

Author

Listed:
  • Jianping Liu

    (Yanshan University
    Hebei Normal University of Science and Technology
    Yanshan University)

  • Shunfu Jin

    (Yanshan University
    Yanshan University)

  • Wuyi Yue

    (Konan University)

Abstract

Cognitive radio networks (CRNs) have been emerged as a solution for realizing dynamic spectrum allocation. Green communication in CRNs will contribute to reducing emission pollution, minimizing operation cost and decreasing energy consumption. The Green CRNs would help in realizing “green spectrum management”. In this paper, we examine the key issue to show how to conserve the energy of base stations in the Green CRNs. In order to meet the demand for more sustainable green communication, we propose a multiple-sleep mode for licensed channels in CRNs. Based on a dynamic spectrum access strategy with the proposed multiple-sleep mode, we establish a continuous-time Markov chain model to capture the stochastic behavior of secondary user (SU) and primary user packets. By using the matrix geometric solution method, we obtain the steady-state probability distribution for the system model. This paper further presents analysis for performance measures in terms of the throughput of SU packets, the average latency of SU packets, the energy saving rate of the system and the channel utilization. We also provide statistical experiments with analysis and simulation to investigate the influences of the service rate of one channel and the sleep timer parameter on the system performance measures. In order to get the utmost out of the spectrum resource and meet the demands for the quality of service requirements of SUs, we construct a system cost function, and improve a Jaya algorithm employing an insect-population model to optimize the proposed energy saving strategy. We also show the optimal combination and global minimum of the system cost by numerical results.

Suggested Citation

  • Jianping Liu & Shunfu Jin & Wuyi Yue, 2019. "Performance evaluation and system optimization of Green cognitive radio networks with a multiple-sleep mode," Annals of Operations Research, Springer, vol. 277(2), pages 371-391, June.
    • Handle: RePEc:spr:annopr:v:277:y:2019:i:2:d:10.1007_s10479-018-3086-6
    DOI: 10.1007/s10479-018-3086-6
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    References listed on IDEAS

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    1. R. Montemanni & D.H. Smith & S.M. Allen, 2001. "Lower Bounds for Fixed Spectrum Frequency Assignment," Annals of Operations Research, Springer, vol. 107(1), pages 237-250, October.
    2. Philipp Rohlfshagen & John Bullinaria, 2010. "Nature inspired genetic algorithms for hard packing problems," Annals of Operations Research, Springer, vol. 179(1), pages 393-419, September.
    3. Yunbae Kim & Ganguk Hwang, 2017. "Delay analysis and optimality of the renewal access protocol," Annals of Operations Research, Springer, vol. 252(1), pages 41-62, May.
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    Cited by:

    1. Evsey Morozov & Stepan Rogozin & Hung Q. Nguyen & Tuan Phung-Duc, 2022. "Modified Erlang Loss System for Cognitive Wireless Networks," Mathematics, MDPI, vol. 10(12), pages 1-20, June.
    2. Yu Zhang & Jinting Wang, 2023. "Effectiveness, fairness and social welfare maximization: service rules for the interrupted secondary users in cognitive radio networks," Annals of Operations Research, Springer, vol. 323(1), pages 247-286, April.
    3. Shi Wang & Hao Sun & Xiaoying Zhu & Tingyue Bian & Yang Yang, 2024. "A novel dynamic channel allocation protocol based on data traffic characterization model in CR-IoT network," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 87(3), pages 625-638, November.

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