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.
References
[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-
385.10.1023/B:COAP.0000044187.23143.bd.
[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.
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