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Performance analysis and optimization research of multi-channel cognitive radio networks with a dynamic channel vacation scheme

  • *Corresponding author: Zhanyou Ma

    *Corresponding author: Zhanyou Ma 
Abstract Full Text(HTML) Figure(12) / Table(1) Related Papers Cited by
  • In order to resolve the issues of channel scarcity and low channel utilization rates in cognitive radio networks (CRNs), some researchers have proposed the idea of "secondary utilization" for licensed channels. In "secondary utilization", secondary users (SUs) opportunistically take advantage of unused licensed channels, thus guaranteeing the transmission performance and quality of service (QoS) of the system. Based on the channel vacation scheme, we analyze a preemptive priority queueing system with multiple synchronization working vacations. Under this discipline, we build a three-dimensional Markov process for this queueing model. Through the analysis of performance measures, we obtain the average queueing length for the two types of users, the mean busy period and the channel utility. By analyzing several numerical experiments, we demonstrate the effect of the parameters on the performance measures. Finally, in order to optimize the system individually and socially, we establish utility functions and provide some optimization results for PUs and SUs.

    Mathematics Subject Classification: Primary: 60K25; Secondary: 90B22.

    Citation:

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  • Figure 1.  The dynamic channel vacation scheme proposed in this paper

    Figure 2.  The running mode of the system

    Figure 3.  The relation of $ E({L_1}) $ to $ {\mu _2} $ and $ c $

    Figure 4.  The relation of $ E({L_2}) $ to $ {\mu _2} $ and $ \theta $

    Figure 5.  The relation of $ {P_d} $ to $ {\lambda _2} $ and $ c $

    Figure 6.  The relation of $ {P_b} $ to $ {\lambda _2} $ and $ c $

    Figure 7.  The relation of $ {P_{wv}} $ to $ {\lambda _2} $ and $ c $

    Figure 8.  The relation of $ {P_u} $ to $ {\mu _2} $ and $ c $

    Figure 9.  The relation of $ {U_{I1}} $ to $ \mu_2 $ and $ \theta $

    Figure 10.  The relation of $ {U_{I2}} $ to $ \mu_2 $ and $ \theta $

    Figure 11.  The relation of $ {U_{s}} $ to $ {\lambda _2} $ and $ c $

    Figure 12.  The relation of $ {U_{s}} $ to $ {\mu_2} $ and $ \theta $

    Table 1.  The Relation of $ E(B) $ to $ {\lambda _2} $ and $ c $

    $ c $ $ \lambda _2 =6 $ $ \lambda _2 =7 $ $ \lambda _2=8 $ $ \lambda _2=9 $ $ \lambda _2=10 $
    3 0.4900 0.4983 0.5052 0.5109 0.5156
    4 0.4678 0.4793 0.4891 0.4975 0.5046
    5 0.4594 0.4731 0.4852 0.4957 0.5049
     | Show Table
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  • [1] Y. ChenP. Liao and Y. Wang, A channel-hopping scheme for continuous rendezvous and data delivery in cognitive radio network, Peer-to-Peer Networking and Applications, 9 (2016), 16-27.  doi: 10.1007/s12083-014-0308-9.
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    [15] Y. Zhao and W. Yue, Performance evaluation and optimization of cognitive radio networks with adjustable access control for multiple secondary users, Journal of Industrial and Management Optimization, 15 (2019), 1-14.  doi: 10.3934/jimo.2018029.
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