CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Performance Assessment of Cognitive Radio Adaptation Engine based on Real-coded Genetic Algorithms

by Ramy A. Fathy, Abdelhalim Zekry, Ahmed A. Abdelhafez
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 13
Year of Publication: 2013
Authors: Ramy A. Fathy, Abdelhalim Zekry, Ahmed A. Abdelhafez
10.5120/12558-9315

Ramy A. Fathy, Abdelhalim Zekry, Ahmed A. Abdelhafez . Performance Assessment of Cognitive Radio Adaptation Engine based on Real-coded Genetic Algorithms. International Journal of Computer Applications. 72, 13 ( June 2013), 51-58. DOI=10.5120/12558-9315

@article{ 10.5120/12558-9315,
author = { Ramy A. Fathy, Abdelhalim Zekry, Ahmed A. Abdelhafez },
title = { Performance Assessment of Cognitive Radio Adaptation Engine based on Real-coded Genetic Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 13 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 51-58 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number13/12558-9315/ },
doi = { 10.5120/12558-9315 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:37:52.043798+05:30
%A Ramy A. Fathy
%A Abdelhalim Zekry
%A Ahmed A. Abdelhafez
%T Performance Assessment of Cognitive Radio Adaptation Engine based on Real-coded Genetic Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 13
%P 51-58
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Adaptation is one of the fundamental functionalities of Cognitive Radio Systems (CRS). Adaptation refers to theability of the radio to adapt its operating parameters in response to varying stimuli. Choice of the best parameter set of the radio to achieve certain objectives in shortest time possible remains one of the most challenging tasks in Cognitive Radio (CR) research. One possible approach to adaptation engine design is based on utilizing Genetic Algorithms (GA) which invoke a combination of exploration and exploitation processes to perform random and directed searches for semi-optimal solutions in the possible solution space. However, conventional Binary-coded Genetic Algorithms (BGA) based adaptation engines used frequently in CR research,are criticized for their slow convergence and response times. Accordingly, Real-coded Genetic Algorithms (RGA) – a specific type of GA – have been implemented in our work, to address this problem. RGA alleviates many of the disadvantages of conventional BGA based implementations. This paper focuses on RGA based adaptation engine implementations' performance assessment compared to conventional BGA based implementations. Performance assessment results indicate that RGA based implementation does demonstrate a superior performance over BGA based implementations; in achieving the best configuration to minimize the link BER with minimum possible transmitted EIRP levels; in the shortest time possible.

References
  1. J. Mitola, III, "Cognitive Radio for Flexible Multimedia Communications", Mobile Multimedia Communications, 1999. (MoMuC '99) 1999 IEEE International Workshop on, pp. 3 –10, 1999.
  2. R. A. Fathy, A. A. Abdelhafez and A. Zekry, "Meta-Heuristic based Adaptation Engine for Cognitive Radio Systems," International Journal of Computer Applications Vol. 64, No. 18 pp:53-60, February 2013. Published by Foundation of Computer Science, New York, USA.
  3. T. W. Rondeau, Application of Artificial Intelligence to Wireless Communications. PhD thesis, Virginia Tech, 2007.
  4. 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.
  5. X. Yang. Engineering Optimization: An Introduction with Metaheuristic Applications. John Wiley & Sons, Inc. 2010.
  6. A. H. A. Ahmed, "Studies on Metaheuristics for Continuous Global Optimization Problems," Kyoto University, Kyoto, Japan, Doctor of Informatics. June 2004.
  7. L. Hansheng, and K. Lishan, "Balance between exploration and exploitation in genetic search," Wuhan University Journal of Natural Sciences,Vol. 4, No. 1 PP. 28-32, 1999.
  8. J. Ludvig, J. Hesser, and R. Manner, Tackling the representation problem by stochastic averaging, in Proceedings of the 7th International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, San Francisco, PP. 196-203, 1996.
  9. Z. Michalewicz, "Genetic Algorithms, Numerical Optimization and Constraints," Proceedings of the Sixth International Conference on Genetic Algorithms, pp. 151-158, 1995.
  10. 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.
  11. 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.
Index Terms

Computer Science
Information Sciences

Keywords

Adaptation Engine Binary-coded Genetic Algorithms Real-coded Genetic Algorithms

Powered by PhDFocusTM