IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/9385693.html
   My bibliography  Save this article

Performance Analysis and Optimization of CRNs Based on Fixed Feedback Probability Mechanism with Two Classes of Secondary Users

Author

Listed:
  • Yuan Zhao
  • Hongyi Li
  • Jiemin Liu

Abstract

In this paper, we conduct a research based on the classified secondary users (SUs). SUs are divided into two categories: higher-priority SU1 and lower-priority SU2, and two types of users generate two types of packets, respectively. Due to the lowest spectrum usage rights of SU2 packets, the SU2 packets’ transmission is easily interrupted by other packets with higher rights. With the purpose of controlling the SU2 packets’ retransmission behavior, we introduce two system parameters, namely, feedback threshold T and feedback probability q . When the amount of SU2 packets in the buffer reaches the feedback threshold T , the interrupted SU2 packets either enter the buffer with probability q for retransmission or leave the channel by probability , where q is a fixed parameter. We construct a three-dimensional Markov model based on the presented retransmission control mechanism and derive some important performance indicators of SU2 packets based on the one-step transfer probability matrix and steady-state distribution. Then, we analyze the impact of some key parameters on the performance indicators through numerical experiments. Finally, we establish a cost function and use particle swarm optimization algorithm to optimize the feedback threshold and feedback probability.

Suggested Citation

  • Yuan Zhao & Hongyi Li & Jiemin Liu, 2019. "Performance Analysis and Optimization of CRNs Based on Fixed Feedback Probability Mechanism with Two Classes of Secondary Users," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-10, September.
  • Handle: RePEc:hin:jnlmpe:9385693
    DOI: 10.1155/2019/9385693
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2019/9385693.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/MPE/2019/9385693.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2019/9385693?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
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnlmpe:9385693. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.