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A Comparative Study of Biology-inspired and Game Theoretical Optimization Algorithms on Power Utilization Efficiency in Cognitive Radio Environment

by Jide Julius Popoola, Olaoluwa Temitope Ojo
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 13
Year of Publication: 2018
Authors: Jide Julius Popoola, Olaoluwa Temitope Ojo
10.5120/ijais2018451756

Jide Julius Popoola, Olaoluwa Temitope Ojo . A Comparative Study of Biology-inspired and Game Theoretical Optimization Algorithms on Power Utilization Efficiency in Cognitive Radio Environment. International Journal of Applied Information Systems. 12, 13 ( May 2018), 29-36. DOI=10.5120/ijais2018451756

@article{ 10.5120/ijais2018451756,
author = { Jide Julius Popoola, Olaoluwa Temitope Ojo },
title = { A Comparative Study of Biology-inspired and Game Theoretical Optimization Algorithms on Power Utilization Efficiency in Cognitive Radio Environment },
journal = { International Journal of Applied Information Systems },
issue_date = { May 2018 },
volume = { 12 },
number = { 13 },
month = { May },
year = { 2018 },
issn = { 2249-0868 },
pages = { 29-36 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number13/1032-2018451756/ },
doi = { 10.5120/ijais2018451756 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:09:12.803539+05:30
%A Jide Julius Popoola
%A Olaoluwa Temitope Ojo
%T A Comparative Study of Biology-inspired and Game Theoretical Optimization Algorithms on Power Utilization Efficiency in Cognitive Radio Environment
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 13
%P 29-36
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper develops two different optimization algorithms for solving problem of power utilization efficiency in cognitive radio environment (CRE). While genetic algorithm was developed as biology-inspired optimization algorithm, load balancing algorithm was developed as game-theoretical optimization algorithm. The two algorithms were developed in MATLAB environment. The developed algorithms were later evaluated to determine their respective power efficiency utilization in CRE. Numerical results obtained reveal that genetic algorithm is about 15% better than load balancing algorithm in term of power utilization efficiency. In addition, the obtained results show that biology-inspired optimization algorithm such as genetic algorithm in which all the parties act together to optimal system is better candidate for spectral resource allocation in CRE than game-theoretical optimization algorithm such as load balancing algorithm where individual acts separately to optimal the system.

References
  1. Popoola, J.J., Ogunlana, O.A., Ajie, F.O., Olakunle O., Akiogbe, O.A., Ani-Initi, S.M. and Omotola, S.K. (2016). Dynamic Spectrum Access: A New Paradigm of Converting Radio Spectrum Wastage to Wealth. International Journal of Engineering Technologies, No. 2, Vol. 3, pp. 124-131.
  2. Popoola, J.J. and van Olst, R. (2013). The performance evaluation of a spectrum sensing implementation using an automatic modulation classification detection method with a Universal Software Radio Peripheral, Expert Systems with Applications, Vol. 40, No. 6, pp. 2165–2173.
  3. Popoola, J.J. and van Olst, R. (2011). A novel modulation-sensing method: Remedy for uncertainty around the practical use of cognitive radio technology. IEEE Vehicular Technology Magazine, Vol. 6, No. 3, pp. 60-69.
  4. Masonta, M.T., Mzyece, M. and Ntlatlapa, N. (2013). Spectrum decision in cognitive radio networks: A survey, IEEE Communications Surveys and Tutorials, Vol. 15, No. 3, pp. 1088-1107.
  5. Akylidz, I.F., Lee, W.Y., Vuran, M.C. and Mohanty, S. (2006). NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey, International Journal of Computer and Telecom. Networking, Vol. 50, No. 13, pp. 2127-2159.
  6. Konig, C., Perez Guirao, M.D. and Lubben, R. (2014). Distributed Indoor Spectrum Occupancy Measurements in the UHF TV Band, IEEE International Conference on Communications, Sydney, NSW, Australia, 10-14 June, pp. 1373-1378.
  7. Moghal, M.R., Khan, M.A. and Bhatti, H.A. (2010). Spectrum optimization in cognitive radios using elitism genetic algorithm, Proceedings of 6th IEEE International Conference Emerging Technologies, Islambad, Pakistan, 18-19, pp. 49-54.
  8. Abdulghfoor, O.B., Ismail, M. and Nordin, R. (2013). Application of game theory to underlay ad-hoc cognitive radio networks: an overview, Proceedings of the IEEE International Conference on Space Science and Communication, Melaka, Malaysia, 1-3 July, pp. 296-301.
  9. Binitha, S. and Sathya, S.S. (2012). A survey of bio inspired optimization algorithms, International Journal of Soft Computing and Engineering, Vol. 2, No. 2, pp. 137-151.
  10. Ji, Z. and Liu, K.J.R. (2007). Dynamic Spectrum Sharing: A Game Theoretical Overview, IEEE Communications Magazine, Vol. 45, No. 5, pp. 88-94.
  11. Sharma, U., Mittal, P. and Nagpal, C.K (2015). Implementing Game Theory in Cognitive Radio Network for Channel Allocation: An Overview, International Journal of Energy, Information and Communications, Vol.6, No. 2, pp.17-22.
  12. Elbeltagi, E., Hegazy, T. and Grierson, D. (2005). Comparison among five evolutionary-based optimization algorithms, Advanced Engineering Informatics, Vol. 19, No. 1, pp. 43-53.
  13. Barough, A.S., Shoubi, M.V. and Skardi, M.J.E. (2012). Application of game theory in solving the construction project conflicts, Procedia-Social and Behavioral Sciences, Vol. 58, pp. 1586-1593.
  14. Yang, X.S. (2005). Engineering optimization via nature-inspired virtual Bee algorithms. In Mira, J., Alvarez, J.R. (eds) Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. IWINAC 2005. Lecture Notes in Computer Science, Vol. 3562. Springer, Berlin, Heidelberg, DOI: org/10.1007/11499305_33.
  15. Singh, R.P. (2011). Solving 0-1 Knapsack problem using Genetic algorithms, IEEE International Conference on Communication Software and Networks, Xi’an, China, 27-29 May, 591-595.
  16. Akyildiz, I.F., Lee, W.Y. Vuran, M.C. and Mohanty, S. (2008). A survey on spectrum management in cognitive radio networks, IEEE Communications Magazine, Vol. 46, No. 4, pp. 40–48.
  17. Zhao, J.H, Li, F. and Zhang, X.X. (2012). Parameter adjustment based on improved genetic algorithm for cognitive radio networks, The Journal of China Universities of Posts and Telecommunications, Vol. 19, No. 3, pp. 22-26.
  18. Gözüpek, D. and Alagöz, F. (2011). Genetic algorithm-based scheduling in cognitive radio networks under interference temperature constraints, International Journal of Communication Systems, Vol. 24, pp. 239-257. DOI: 10.1002/dac.1152.
  19. Rondeau, T.W., Le, B., Rieser, C.J. and Bostian, C.W. (2004). Cognitive Radios with Genetic Algorithms: Intelligent Control of Software Defined Radios, Proceeding of the SDR Technical Conference and Product Exposition, pp. C–3–C–8. Online [Available] https://pdfs.semanticscholar.org/f5ea/32eb879fae38911ccf18f324cb6a37de27c2.pdf. Access on March 18, 2018.
  20. Chen, S, Newman, T.R. Evans, J.B. and Wyglinski, A.M (2010). Genetic algorithm-based optimization for cognitive radio networks. IEEE Sarnoff Symposium, Princeton, New Jersey, USA, 12-14 April. DOI: 10.1109/SARNOF.2010.5469780.
  21. Hauris, J.F., He, D., Michael, G. and Ozbay, C. (2007). Cognitive radio and RF communications design optimization using genetic algorithms, Proceedings of IEEE Military Communications Conference, Orlando, Florida, USA, 29-31 October, pp. 1-6.
  22. Pandit, S. and Singh, G. (2013). Spectrum sharing in cognitive radio using game theory, Proceedings of the 3rd International Advance Computing Conference, Ghaziabad, India, 22-23 February, pp. 1505-1508.
  23. Rajasekharan, J. and Koivunen, V. (2015). Cooperative game-theoretic approach to spectrum sharing in cognitive radios, Signal Processing, Vol. 106, pp. 15-29.
  24. Nolan, K., Sutton, P., Doyle, L., Rondeau, T., Le, B. and Bostian, C. (2007). Dynamic Spectrum Access and Coexistence Experiences Involving Two Independently Developed Cognitive Radio Testbeds, Proceedings of 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Dublin, Ireland, 17-20 April, pp. 270-275.
  25. Popoola, J.J. and R. van Olst (2015). Demonstration of Graphical User Interface Spectrum Algorithm using some Wireless Systems in South Africa, Journal of Applied Science & Processing Engineering, Vol. 2, No. 2, pp. 44-63.
Index Terms

Computer Science
Information Sciences

Keywords

Radio spectrum management dynamic spectrum access optimization evolutionary algorithms game theory

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