Using Enhanced Stimulated Annealing Algorithm to Performing Investigation in Cognitive Radio Network

Using Enhanced Stimulated Annealing Algorithm to Performing Investigation in Cognitive Radio Network



  • 2020

  • November-December

  • Research

    Technical Paper Publishing Sites , Journals for Publication of Research Paper , Best Journal to Publish Research Paper , Research Paper Publication Sites , Best International Journal for Paper Publication , Best Journals to Publish Papers in India , Journal Publication Sites , Research Paper Publication Sites



    This document presents the optimization of quality of service for
    unlicensed subscribers anticipating the QoS of the primary users as well. The
    concept of cognitive radio came into existence for the exclusive use of the unused
    spectrum space by the licensed users. The unlicensed user or the secondary users
    use the unoccupied spectrum spaces of licensed user's spectrum for the effective
    usage of the spectrum. The use of live streaming, Voice over Internet
    protocol(VoIP) and multi media applications which are delay sensitive
    applications throughout the session. The constraints like delay and throughput
    and the quality of service are affected due to these applications. To overcome
    these constraints the tradeoff between these parameters must be employed. In this
    paper, an extension to the non-work conservation policy is implemented in
    Cognitive radio network(CRN) by using the optimization algorithm known as
    Enhanced stimulated annealing(ESA) algorithm to bring forth the tradeoff
    between delay and throughput.
    Keywords: Cognitive Radio, Delay, Enhanced stimulated annealing Algorithm,
    Optimization, Throughput, Trade-off.
    [1] Haykin, Simon. (2005). Haykin, S.: Cognitive radio: Brain-empowered wireless communications. IEEE JSAC 23(2), 201-220. Selected Areas in
    Communications, IEEE Journal on. 23. 201 - 220. 10.1109/JSAC.2004.839380.
    [2] Adel M. Elmahdy, Amr El-Keyi, Tamer ElBatt, Senior Member, IEEE, and Karim G. Seddik, Senior Member, “Optimizing Cooperative Cognitive Radio
    Networks Performance with Primary QoS Provisioning”, IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 65, NO. 4, APRIL 2017.
    [3] J.kanti, G. S. Tomar, and A. Bagwari, “A novel multiple antennas based centralized spectrum sensing technique,” in Transactions on Computational
    Science, vol. 10220 of Lecture Notes in Computer Science, pp. 64–85, Springer, Berlin, 2017.
    [4] A. Bagwari, G. S. Tomar, and S. Verma, “Cooperative spectrum sensing based on two-stage detectors with multiple energy detectors and adaptive double
    threshold in cognitive radio networks,” Canadian Journal of Electrical and Computer Engineering, vol. 36, no. 4, Article ID 6776586, pp. 172– 180, 2013.
    [5] L. Kleinrock, Queueing Systems: Theory, vol. 1. New York, NY, USA: Wiley, 1975.
    [6] Ben-Ameur, Walid.(2004). Computing the Initial Temperature of Simulated Annealing. Computational Optimization and Applications.29.369-
    [7] G.E. Nasr, A. Harb and G. Meghabghab
    “Enhanced Simulated Annealing Techniques for MultiprocessorScheduling” Proceedings of the 12th International FLAIRS Conference.
    [8] MohdRizam Abu Bakar, Abdul Jabbar KhudhurBakheet, Farah Kamil, Bayda Atiya Kalaf, Iraq T. Abbas, and Lee Lai Soon, “Enhanced Simulated
    Annealing for Solving Aggregate Production Planning,” Mathematical Problems in Engineering, vol. 2016, Article ID 1679315.
    [9] M. Ashour, A. A. El-Sherif, T. ElBatt, and A. Mohamed, “Cognitiveradio networks with probabilistic relaying: Stable throughput and delaytradeoffs,”
    IEEE Trans. Commun., vol. 63, no. 11, pp. 4002–4014,Nov. 2015.
    [10]A. M. Elmahdy, A. El-Keyi, T. ElBatt, and K. G. Seddik, “On optimizingcooperative cognitive user performance under primary QoSconstraints,” in Proc.

    IEEE Wireless Commun. Netw. Conf. (WCNC),Apr. 2016, pp. 1–7.

    Using Enhanced Stimulated Annealing Algorithm to Performing Investigation in Cognitive Radio Network