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Performance analysis and optimization for cognitive radio networks with a finite primary user buffer and a probability returning scheme

  • * Corresponding author: Yuan Zhao

    * Corresponding author: Yuan Zhao 
Abstract / Introduction Full Text(HTML) Figure(7) / Table(3) Related Papers Cited by
  • In this paper, in order to reduce possible packet loss of the primary users (PUs) in cognitive radio networks, we assume there is a buffer with a finite capacity for the PU packets. At the same time, focusing on the packet interruptions of the secondary users (SUs), we introduce a probability returning scheme for the interrupted SU packets. In order to evaluate the influence of the finite buffer setting and the probability returning scheme to the system performance, we construct and analyze a discrete-time Markov chain model. Accordingly, we determine the expressions of some important performance measures of the PU packets and the SU packets. Then, we show numerical results to evaluate how the buffer setting of the PU packets and the returning probability influence the system performance. Moreover, we optimize the system access actions of the SU packets. We determine their individually and the socially optimal strategies by considering different buffer settings for PU packets and different returning probabilities for SU packets. Finally, a pricing policy by introducing an admission fee is also provided to coincide the two optimal strategies.

    Mathematics Subject Classification: Primary: 68M10, 68M20; Secondary: 60J10.

    Citation:

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  • Figure 1.  System actions of PU packets and SU packets

    Figure 2.  Average queue length $ E_{PU} $ of PU packets

    Figure 3.  Throughput $ \theta_{PU} $ of PU packets

    Figure 4.  Blocking rate $ \beta_{SU} $ of SU packets

    Figure 5.  Average delay $ \delta_{SU} $ of SU packets

    Figure 6.  Function $ F_I(\lambda_2) $ of the individual net benefit

    Figure 7.  Function $ F_S(\lambda_2) $ of the social net benefit

    Table 1.  Notations for the system model

    Symbol Explanation
    $ \lambda_1 $ Arrival rate of the PU packets
    $ \lambda_2 $ Arrival rate of the SU packets
    $ \mu_1 $ Transmission rate of the PU packets
    $ \mu_2 $ Transmission rate of the SU packets
    $ K_1 $ Capacity of the PU buffer
    $ K_2 $ Capacity of the SU buffer
    $ q $ Returning probability for the interrupted SU packets
    $ P_n $ Number of PU packets in the system at the instant $ t=n^+ $
    $ S_n $ Number of SU packets in the system at the instant $ t=n^+ $
     | Show Table
    DownLoad: CSV

    Table 2.  Numerical results for the individually and socially optimal strategies

    $ K_1 $ $ K_2 $ $ q $ $ \lambda_i $ $ r_i $ $ \lambda_s $ $ r_s $
    min max min max
    0 5 0.4 0.26 0.27 0.52 0.54 0.18 0.36
    2 5 0.4 0.15 0.16 0.30 0.32 0.10 0.20
    0 5 0.8 0.30 0.31 0.60 0.62 0.19 0.38
    2 5 0.8 0.20 0.21 0.40 0.42 0.12 0.24
    0 8 0.8 0.32 0.33 0.64 0.66 0.22 0.44
    2 8 0.8 0.23 0.24 0.46 0.48 0.15 0.30
     | Show Table
    DownLoad: CSV

    Table 3.  Numerical results for the admission fee

    $ K_1 $ $ K_2 $ $ q $ $ \lambda_s $ $ f $
    0 5 0.4 0.18 1.9650
    2 5 0.4 0.10 1.7917
    0 5 0.8 0.19 5.8778
    2 5 0.8 0.12 5.4328
    0 8 0.8 0.22 6.3116
    2 8 0.8 0.15 5.9168
     | Show Table
    DownLoad: CSV
  • [1] A. Alfa, Queueing Theory for Telecommunications: Discrete Time Modelling of a Single Node System, Springer, New York, 2010. doi: 10.1007/978-1-4419-7314-6.
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    [19] Y. Zhao and W. Yue, Performance evaluation of cognitive radio networks with a finite buffer setting for primary users, in Queueing Theory and Network Applications (eds. W. Yue, Q. Li, S. Jin and Z. Ma), Springer, (2017), 168-179.
    [20] Y. Zhao and W. Yue, Performance analysis and optimization of cognitive radio networks with retransmission control, Optimization Letters, 12 (2018), 1281-1300.  doi: 10.1007/s11590-017-1119-8.
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