Cognitive Radio Oriented Wireless Networks. 10th International Conference, CROWNCOM 2015, Doha, Qatar, April 21–23, 2015, Revised Selected Papers

Research Article

Effective Capacity and Delay Optimization in Cognitive Radio Networks

Download130 downloads
  • @INPROCEEDINGS{10.1007/978-3-319-24540-9_3,
        author={Mai Abdel-Malek and Karim Seddik and Tamer ElBatt and Yahya Mohasseb},
        title={Effective Capacity and Delay Optimization in Cognitive Radio Networks},
        proceedings={Cognitive Radio Oriented Wireless Networks. 10th International Conference, CROWNCOM 2015, Doha, Qatar, April 21--23, 2015, Revised Selected Papers},
        proceedings_a={CROWNCOM},
        year={2015},
        month={10},
        keywords={Cognitive radios Effective capacity Delay constraints Optimization Quality of service (QoS)},
        doi={10.1007/978-3-319-24540-9_3}
    }
    
  • Mai Abdel-Malek
    Karim Seddik
    Tamer ElBatt
    Yahya Mohasseb
    Year: 2015
    Effective Capacity and Delay Optimization in Cognitive Radio Networks
    CROWNCOM
    Springer
    DOI: 10.1007/978-3-319-24540-9_3
Mai Abdel-Malek1,*, Karim Seddik2,*, Tamer ElBatt,*, Yahya Mohasseb,*
  • 1: Nile University
  • 2: American University in Cairo
*Contact email: m.elkady@nileu.edu.eg, kseddik@aucegypt.edu, telbatt@ieee.org, mohasseb@ieee.org

Abstract

In this paper, we study the fundamental trade-off between delay-constrained primary and secondary users in cognitive radio networks. In particular, we characterize and optimize the trade-off between the secondary user (SU) effective capacity and the primary user (PU) average packet delay. Towards this objective, we employ Markov chain models to quantify the SU effective capacity and average packet delay in the PU queue. Afterwards, we formulate two constrained optimization problems to maximize the SU effective capacity subject to an average PU delay constraint. In the first problem, we use the spectrum sensing energy detection threshold as the optimization variable. In the second problem, we extend the problem and optimize also over the transmission powers of the SU. Interestingly, these complex non-linear problems are proven to be quasi-convex and, hence, can be solved efficiently using standard optimization tools. The numerical results reveal interesting insights about the optimal performance compared to the unconstrained PU delay baseline system.