Performance Evaluation of Qos Parameters in Cognitive Radio Using Genetic Algorithm
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.
[1] 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.
[2] 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
[3] FCC, "Spectrum policy task force report," ET Docket No. 02-155, Nov.
2002.
[4] Joint Tactical radio Systems, "Software communications architecture
specification," November 2002.
[5] 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
[6] Spectrum Policy Task Force, "Report of the spectrum policy
workgroup," November 2002. [Online]. Available: http://www.fcc.gov
/sptf/files/SEWGFinalReport\_1.pdf
[7] 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.
[8] R.L. Haupt, S.E Haupt, Practical Genetic Algorithms. Wiley, 2004.
[9] 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.
[1] 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.
[2] 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
[3] FCC, "Spectrum policy task force report," ET Docket No. 02-155, Nov.
2002.
[4] Joint Tactical radio Systems, "Software communications architecture
specification," November 2002.
[5] 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
[6] Spectrum Policy Task Force, "Report of the spectrum policy
workgroup," November 2002. [Online]. Available: http://www.fcc.gov
/sptf/files/SEWGFinalReport\_1.pdf
[7] 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.
[8] R.L. Haupt, S.E Haupt, Practical Genetic Algorithms. Wiley, 2004.
[9] 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.
@article{"International Journal of Information, Control and Computer Sciences:60064", author = "Maninder Jeet Kaur and Moin Uddin and Harsh K. Verma", title = "Performance Evaluation of Qos Parameters in Cognitive Radio Using Genetic Algorithm", abstract = "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.", keywords = "Cognitive Radio, Fitness Functions, Fuzzy Logic,Quality of Service (QoS)", volume = "4", number = "10", pages = "1588-6", }