Published October 28, 2010
| Version 11014
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Performance Evaluation of Qos Parameters in Cognitive Radio Using Genetic Algorithm
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The efficient use of available licensed spectrum is
becoming more and more critical with increasing demand and usage
of the radio spectrum. This paper shows how the use of spectrum as
well as dynamic spectrum management can be effectively managed
and spectrum allocation schemes in the wireless communication
systems be implemented and used, in future. This paper would be an
attempt towards better utilization of the spectrum. This research will
focus on the decision-making process mainly, with an
assumption that the radio environment has already been sensed and
the QoS requirements for the application have been specified either
by the sensed radio environment or by the secondary user itself. We
identify and study the characteristic parameters of Cognitive Radio
and use Genetic Algorithm for spectrum allocation. Performance
evaluation is done using MATLAB toolboxes.
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References
- D. Cabric, S. M. Mishra, and R. Brodersen, "Implementation issues in spectrum sensing for cognitive radios," in Proc. 38th Asilomar Conf. Signals, Systems and Computers, Pacific Grove, CA, Nov. 2004, pp. 772-776.
- I. F. Akyildiz, W. Y. Lee, M. C. Vuran, S. Mohanty, "NeXt generation dynamic spectrum access cognitive radio wireless networks: A survey," Computer Networks, 50, 2006, pp 2127-2159
- FCC, "Spectrum policy task force report," ET Docket No. 02-155, Nov. 2002.
- Joint Tactical radio Systems, "Software communications architecture specification," November 2002.
- R. Etkin, A. Parekh, and D.Tse, "Spectrum sharing for unlicensed bands," in IEEE International Symposium on New Frontiers in Dynamic Spectrum Access, 2005, pp 251-258
- Spectrum Policy Task Force, "Report of the spectrum policy workgroup," November 2002. [Online]. Available: http://www.fcc.gov /sptf/files/SEWGFinalReport\_1.pdf
- C.J. Rieser, "Biologically inspired cognitive radio engine model utilizing distributed genetic algorithms for secure and robust wireless communications and networking," Ph.D dissertation, Virginia Polytechnic Institute and State University, April 2004.
- R.L. Haupt, S.E Haupt, Practical Genetic Algorithms. Wiley, 2004.
- H. Lu and G.G. Yen, "Multiobjective Optimization Design via Genetic Algorithm," IEEE Proceedings of the International Conference on Control Applications, 2001, pp.1190-1195. [10] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Professional, 1989 [11] M. Mitchell, An Introduction to Genetic Algorithm. The MIT Press, 1998. [12] http://cfpm.org/~david/talks/ga2005/web/ga-java-site/cs.felk. cvut.cz /_xobitko/ga/main.html [13] B. Ackland, D. Raychaudhuri, M. Bushnell, C. Rose, I. Seskar, T. Sizer, D. Samardzija, J. Pastalan, A. Siegel, J. Laskar, S. Pinel, K. Lim, "High Performance Cognitive Radio Platform with Integrated Physical and Network Layer Capabilities," Georgia Institute of Technology Interim Technical Report, July, 2005. [14] T.Newman, B.Barker, A. Wyglinski, A.Agah, J.Evans, G.Minden, Cognitive engine implementation for wireless multicarrier transceivers. Wiley Wireless Communications and Mobile Computing edition, 2007.