Genetic algorithm application in optimizing transmission parameters on adaptive mechanism of cognitive radio

Genetic algorithm application in optimizing transmission parameters on adaptive mechanism of cognitive radio

Cognitive radio (CR) technology introduces a revolutionary in wireless communication network and it is capable to operate in a continuously varying radio frequency (RF) environment that depends on multiple parameters. CR has the capability to sense, learn the environment and adapt intelligently to t...

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Main Author: Tan, Jui Ang
Format: Thesis
Language:English
Published: 2009
Subjects:
TK Electrical engineering
Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/12424/1/TanJuiAngMFKE2009.pdf
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id my-utm-ep.12424
record_format uketd_dc
institution Universiti Teknologi Malaysia
collection UTM Institutional Repository
language English
topic TK Electrical engineering
Electronics Nuclear engineering
spellingShingle TK Electrical engineering
Electronics Nuclear engineering
Tan, Jui Ang
Genetic algorithm application in optimizing transmission parameters on adaptive mechanism of cognitive radio
description Cognitive radio (CR) technology introduces a revolutionary in wireless communication network and it is capable to operate in a continuously varying radio frequency (RF) environment that depends on multiple parameters. CR has the capability to sense, learn the environment and adapt intelligently to the most appropriate way for providing the optimize service that suit to the user’s requirements. Recent researches show that Genetic algorithms (GAs) that rooted in biological inspired are viable implementation technique for CR engine to optimize transmission parameters in a given wireless environment. In this work, GA is applied in adaptive mechanism of CR to perform optimization on transmitter parameters for physical (PHY) layer. The objective of optimization is to obtained optimum set of transmission parameters in order to meet quality of service (QoS) that defined by user in term of minimum transmit power, minimum bit error rate (BER) and maximum throughput. Fitness functions are developed to evaluate the performance of the GA in relation to transmission parameters that characterized. The characterization involves deriving chromosome structure that consists of transmission parameters gene. Finally, a MATLAB® code is developed for simulating the GA operations to achieve optimum set of transmission parameters for optimal radio communications. Simulation results show fitness score for minimum transmit power is 0.927174 with optimum transmit power 0.1768 mW and modulation 64 QAM. While the fitness score for minimum BER is 0.852842 with optimum transmit power 0.74 mW and modulation 8 QAM. Lastly, the fitness score for maximum throughput is 0.952603 with optimum transmit power 0.7144 mW and modulation 64 QAM.
format Thesis
qualification_level Master's degree
author Tan, Jui Ang
author_facet Tan, Jui Ang
author_sort Tan, Jui Ang
title Genetic algorithm application in optimizing transmission parameters on adaptive mechanism of cognitive radio
title_short Genetic algorithm application in optimizing transmission parameters on adaptive mechanism of cognitive radio
title_full Genetic algorithm application in optimizing transmission parameters on adaptive mechanism of cognitive radio
title_fullStr Genetic algorithm application in optimizing transmission parameters on adaptive mechanism of cognitive radio
title_full_unstemmed Genetic algorithm application in optimizing transmission parameters on adaptive mechanism of cognitive radio
title_sort genetic algorithm application in optimizing transmission parameters on adaptive mechanism of cognitive radio
granting_institution Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department Faculty of Electrical Engineering
publishDate 2009
url http://eprints.utm.my/id/eprint/12424/1/TanJuiAngMFKE2009.pdf
_version_ 1747814936675876864
spelling my-utm-ep.124242018-06-25T08:57:45Z Genetic algorithm application in optimizing transmission parameters on adaptive mechanism of cognitive radio 2009-05 Tan, Jui Ang TK Electrical engineering. Electronics Nuclear engineering Cognitive radio (CR) technology introduces a revolutionary in wireless communication network and it is capable to operate in a continuously varying radio frequency (RF) environment that depends on multiple parameters. CR has the capability to sense, learn the environment and adapt intelligently to the most appropriate way for providing the optimize service that suit to the user’s requirements. Recent researches show that Genetic algorithms (GAs) that rooted in biological inspired are viable implementation technique for CR engine to optimize transmission parameters in a given wireless environment. In this work, GA is applied in adaptive mechanism of CR to perform optimization on transmitter parameters for physical (PHY) layer. The objective of optimization is to obtained optimum set of transmission parameters in order to meet quality of service (QoS) that defined by user in term of minimum transmit power, minimum bit error rate (BER) and maximum throughput. Fitness functions are developed to evaluate the performance of the GA in relation to transmission parameters that characterized. The characterization involves deriving chromosome structure that consists of transmission parameters gene. Finally, a MATLAB® code is developed for simulating the GA operations to achieve optimum set of transmission parameters for optimal radio communications. Simulation results show fitness score for minimum transmit power is 0.927174 with optimum transmit power 0.1768 mW and modulation 64 QAM. While the fitness score for minimum BER is 0.852842 with optimum transmit power 0.74 mW and modulation 8 QAM. Lastly, the fitness score for maximum throughput is 0.952603 with optimum transmit power 0.7144 mW and modulation 64 QAM. 2009-05 Thesis http://eprints.utm.my/id/eprint/12424/ http://eprints.utm.my/id/eprint/12424/1/TanJuiAngMFKE2009.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering 3G AMERICAS (2004). Technical Analysis and Position Paper on the Regulatory Issues between Licensed and Unlicensed Spectrum. 3G AMERICAS. Bin Le, Francisco A. G. Rodriguez, Qinqin Chen, Bin Philip Li, Feng Ge, Mustafa ElNainay, Thomas W. Rondeau, and Charles W. Bostian (2007). A Public Safety Cognitive Radio Node. SDR Forum Technical Conference 2007. Christian James Rieser (2004). Biologically Inspired Cognitive Radio Engine Model Utilizing Distributed Genetic Algorithms for Secure and Robust Wireless Communications and Networking. Ph.D. Thesis. Virginia Polytechnic Institute and State University. C. M. Fonseca and P. J. Fleming (1993). Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion, and Generalization. Proc. Int. Conf. Genetic Algorithms. 416 – 423. Danijela Branislav Cabric and Robert W. Brodersen (2007). Cognitive Radios: System Design Perspective. Ph.D. Thesis. University of California, Berkeley. David Maldonado, Bin Le, Akilah Hugine, Thomas W. Rondeau, Charles W. Bostian (2005). Cognitive Radio Applications to Dynamic Spectrum Allocation. First IEEE International Symposium. 597 – 600. D.E. Goldberg (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Pub Co. Fed. Commun. Comm (2003). Et docket-322. Fed. Commun. Comm. Feng Ge, Qinqin Chen, Ying Wang, Charles W. Bostian, Thomas W. Rondeau and Bin Le (2008). Cognitive Radio: From Spectrum Sharing to Adaptive. Aerospace Conference, 2008 IEEE. 1-8 March. 1 – 10. J. G. Proakis (2000). Digital Communications. (4th ed.). McGraw-Hill. J. Mitola and G. Q. Maguire (1999). Cognitive radio: Making software radios more personal. IEEE Pers. Commun, vol. 6. 13–18. J. Mitola (2000). Software Radio Architecture: Object Oriented Approaches to Wireless Systems Engineering. John Wiley and Sons. J. Yang (2004). Spatial channel characterization for cognitive radios. Master Thesis. University of California, Berkeley. K. N. Steadman, A. D. Rose, and T. T. Nguyen (2007). Dynamic Spectrum Sharing Detectors. New Frontiers in Dynamic Spectrum Access Networks (DySPAN). Dublin, Ireland. Mubbashar and Sohaib (2008). Decision Making Techniques For Cognitive Radio. Master Thesis. Blekinge Institute of Technology. Timothy R. Newman, Rakesh Rajbanshi, Alexander M. Wyglinski, Joseph B. Evans, and Gary J. Minden (2007). Population Adaptation for Genetic Algorithm-based Cognitive Radios. CrownCom 2nd International Conference. 1-3 August. 279 - 284 T. W. Rondeau, C. W. Bostian, D. Maldonado, A. Ferguson, S. Ball, S. F. Midkiff, B. Le (2005). Cognitive Radios in Public Safety and Spectrum Management. 33rd Research Conference on Communication, Information and Internet Policy.

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