Advanced Search
Article Contents
Article Contents

Equilibrium analysis of an opportunistic spectrum access mechanism with imperfect sensing results

Abstract Full Text(HTML) Figure(7) / Table(2) Related Papers Cited by
  • In order to reduce the average delay of secondary user (SU) packets and adapt to various levels of tolerance for transmission interruption, we propose a novel opportunistic channel access mechanism with admission threshold and probabilistic feedback in cognitive radio networks (CRNs). Considering the preemptive priority of primary user (PU) packets, as well as the sensing errors of missed detection and false alarm caused by SUs, we establish a type of priority queueing model in which two classes of customers may interfere with each other. Based on this queueing model, we evaluate numerically the proposed mechanism and then present the system performance optimization. By employing a matrix-geometric solution, we derive the expressions for some important performance measures. Then, by building a reward function, we investigate the strategies for both the Nash equilibrium and the social optimization. Finally, we provide a pricing policy for SU packets to coordinate these two strategies. With numerical experiments, we verify the effectiveness of the proposed opportunistic channel access mechanism and the rationality of the proposed pricing policy.

    Mathematics Subject Classification: Primary: 68M10, 68M20; Secondary: 60J28.


    \begin{equation} \\ \end{equation}
  • 加载中
  • Figure 1.  Transmission process of a PU packet

    Figure 2.  Transmission process of an SU packet

    Figure 3.  Throughput $\phi$ of SU packets

    Figure 4.  Block rate $\beta$ of SU packets

    Figure 5.  Average delay $W(\lambda_{su})$ of SU packets

    Figure 6.  Change trend for the net benefit of an SU packet

    Figure 7.  Change trend for the social welfare

    Table 1.  Parameter settings in numerical experiments

    slot1 ms
    transmission rate in physical layer11 Mbps
    arrival rate of SU packets0.3
    mean size of an SU packet1760 Byte
    arrival rate of PU packets0.05
    mean size of a PU packet2010 Byte
    feedback probability0.0-1.0
    energy threshold1.0-7.0
    simulation scale3 million slots
    sensing time0.1 ms
    sensing frequency10 times/ms
     | Show Table
    DownLoad: CSV

    Table 2.  Numerical results for the admission price $F$

    Admission threshold $H$Admission probability $r$Feedback probability $q$Admission price $F$
     | Show Table
    DownLoad: CSV
  • [1] O. AltradS. MuhaidatA. Al-DweikA. Shami and P. Yoo, Opportunistic spectrum access in cognitive radio networks under imperfect spectrum sensing, IEEE Transactions on Vehicular Technology, 63 (2014), 920-925.  doi: 10.1109/TVT.2013.2281334.
    [2] S. AtapattuC. Tellambura and H. Jiang, Energy detection based cooperative spectrum sensing in cognitive radio networks, IEEE Transactions on Wireless Communications, 10 (2011), 1232-1241.  doi: 10.1109/TWC.2011.012411.100611.
    [3] A. BhowmickM. DasJ. BiswasS. Roy and S. Kundu, Throughput optimization with cooperative spectrum sensing in cognitive radio network, Proceeding of the 4th IEEE International Advance Computing Conference, (2014), 329-332.  doi: 10.1109/IAdCC.2014.6779343.
    [4] G. Bochechka and V. Tikhvinskiy, Spectrum occupation and perspectives millimeter band utilization for 5G networks, Proceeding of ITU Kaleidoscope Academic Conference: Living in a Converged World-Impossible without Standards?, (2014), 69-72.  doi: 10.1109/Kaleidoscope.2014.6858482.
    [5] S. GeS. Jin and W. Yue, Throughput analysis for the opportunistic channel access mechanism in CRNs with imperfect sensing results, Proceeding of Queueing Theory and Network Applications, 383 (2015), 55-62.  doi: 10.1007/978-3-319-22267-7_5.
    [6] G. GhoshS. Chatterjee and P. Das, Cognitive radio and dynamic spectrum access-A study, International Journal of Next-Generation Networks, 6 (2014), 43-60.  doi: 10.5121/ijngn.2014.6104.
    [7] A. GorcinK. QaraqeH. Celebi and H. Arslan, An adaptive threshold method for spectrum sensing in multi-channel cognitive radio networks, Proceeding of the 17th International Conference on Telecommunications, (2010), 425-429.  doi: 10.1109/ICTEL.2010.5478783.
    [8] R. Hassin and M. Haviv, To Queue or Not To Queue: Equilibrium Behavior in Queueing Systems, Springer, Boston, 2003. doi: 10.1007/978-1-4615-0359-0.
    [9] H. HuH. ZhangY. Xu and N. Li, Minimum transmission delay via spectrum sensing in cognitive radio networks, Proceeding of IEEE Wireless Communications and Networking Conference, (2013), 4101-4106. 
    [10] H. HuH. Zhang and H. Yu, Efficient spectrum sensing with minimum transmission delay in cognitive radio networks, Mobile Networks and Applications, 19 (2014), 487-501.  doi: 10.1007/s11036-014-0528-5.
    [11] M. KahvandM. Soleimani and M. Dabiranzohouri, Channel selection in cognitive radio networks: A new dynamic approach, Proceeding of the 11th IEEE Malaysia International Conference on Communications, (2013), 407-411. 
    [12] J. Kim and G. Hwang, Cross-layer modeling and optimization of multi-channel cognitive radio networks under imperfect channel sensing, Journal of Industrial & Management Optimization, 11 (2015), 763-777.  doi: 10.3934/jimo.2015.11.807.
    [13] K. KimK. Kwak and B. Choi, Performance analysis of opportunistic spectrum access protocol for multi-channel cognitive radio networks, Journal of Communications and Networks, 15 (2013), 77-86.  doi: 10.1109/JCN.2013.000013.
    [14] H. Li and Z. Han, Socially optimal queuing control in cognitive radio networks subject to service interruptions: To queue or not to queue?, IEEE Transactions on Wireless Communications, 10 (2011), 1656-1666. 
    [15] Y. LiangK. ChenG. Li and P. Mahonen, Cognitive radio networking and communications: An overview, IEEE Transactions on Vehicular Technology, 60 (2011), 3386-3407.  doi: 10.1109/TVT.2011.2158673.
    [16] Y. LiangY. ZengE. Peh and A. Hoang, Sensing-throughput tradeoff for cognitive radio networks, IEEE Transactions on Wireless Communications, 7 (2008), 1326-1337.  doi: 10.1109/ICC.2007.882.
    [17] M. Neuts, Matrix-Geometric Solutions in Stochastic Models: An Algorithmic Approach, Courier Dover Publications, Baltimore, 1981.
    [18] S. TanJ. Zeidler and B. Rao, Opportunistic spectrum access for cognitive radio networks with multiple secondary users, IEEE Transactions on Wireless Communications, 12 (2013), 6214-6227. 
    [19] N. TranC. DoS. Moon and C. Hong, Pricing mechanisms and equilibrium behaviors of noncooperative users in cognitive radio networks, Proceeding of IEEE Global Communications Conference, (2013), 913-918.  doi: 10.1109/GLOCOM.2013.6831190.
    [20] Y. WangJ. LiL. HuangY. JingA. Georgakopoulos and P. Demestichas, 5G mobile: Spectrum broadening to higher-frequency bands to support high data rates, IEEE Vehicular Technology Society, 9 (2014), 39-46.  doi: 10.1109/MVT.2014.2333694.
    [21] B. Wang and K. Liu, Advances in cognitive radio networks: A survey, IEEE Journal of Selected Topics in Signal Processing, 5 (2011), 5-23. 
  • 加载中




Article Metrics

HTML views(630) PDF downloads(113) Cited by(0)

Access History

Other Articles By Authors



    DownLoad:  Full-Size Img  PowerPoint