Abstract

Cognition is a high level mental faculty of the brain that includes functions like adaptation, learning, deciding, and others. Accordingly, a Cognitive Radio must have capabilities that mimic such Cognitive functions. As one of the fundamental cognitive abilities of the radio, this paper proposes a novel adaptation method; which uses Real-coded Genetic Algorithms (RGA) to adapt physical layer radio parameters in response to varying environmental conditions and different user services. The adaptation method is applied in a single objective optimization setting – that's the minimization of BER. Minimum transmitted EIRP levels of the resulting solutions are achieved by using a special Power Limiting Algorithm (PLA) which increments the maximum transmitted allowable EIRP levels during the engine run, if it experienced a slow convergence towards the optimal required solution. Results have indicated the success of the engine in adapting the physical layer radio parameters in response to varying environmental conditions and different user services to minimize resulting link BER, with the minimum possible transmitted EIRP levels.

References

• J. Mitola, III, "Cognitive Radio for Flexible Multimedia Communications", Mobile Multimedia Communications, 1999. (MoMuC '99) 1999 IEEE International Workshop on, pp. 3 –10, 1999.
• Federal Communications Commission, "Spectrum Policy Task Force, " Rep. ET Docket no. 02-135, Nov. 2002.
• T. W. Rondeau, Application of Artificial Intelligence to Wireless Communications. PhD thesis, Virginia Tech, 2007.
• T. R. Newman, B. A. Barker, A. M. Wyglinski, A. Agah, J. B. Evans, and G. J. Minden, " Cognitive Engine Implementation for Wireless Multicarrier Transceivers," Wiley Journal on Wireless Communications and Mobile Computing, vol. 7, no. 9, pp. 1129–1142, 2007.
• Z. Zhao, S. Xu, S. Zheng, and J. Shang, "Cognitive Radio Adaptation using Particle Swarm Optimization," Wiley Journal on Wireless Communications and Mobile Computing, vol. 9, no. 7, pp. 875–881, 2009.
• S. Haykin, "Cognitive Radio: Brain-Empowered Wireless Communications," IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, Feb. 2005.
• L. D. Davis and M. Mitchell. Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York, 1991.
• X. Yang. Engineering Optimization: An Introduction with Metaheuristic Applications. John Wiley & Sons, Inc. 2010.
• Z. Michalewicz, "Genetic Algorithms, Numerical Optimization and Constraints," Proceedings of the Sixth International Conference on Genetic Algorithms, pp. 151-158, 1995.
• P. Kaelo and M. M. Ali, "Integrated Crossover Rules in Real Coded Genetic Algorithms," European Journal of Operational Research, vol. 176, no. 1, pp. 60-76, Jan. 2007.
• R. A. E. Makinen, J. Periaux and J. Toivanen, "Multidisciplinary Shape Optimization in Aerodynamics and Electromagnetic using Genetic Algorithm," International Journal for Numerical Methods in Fluids, vol. 30, no. 2, pp. 149-159, 1999.