Cognitive Radio Parameter Adaptation Using Multi-objective Evolutionary Algorithm

  • Deepak K. Tosh
  • Siba K. Udgata
  • Samrat L. Sabat
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 130)


Cognitive Radio (CR) is an intelligent Software Defined Radio (SDR) that can alter its transmission parameters according to predefined objectives by sensing the dynamic wireless environment. In this paper, we propose a method to determine the necessary transmission parameters for a multicarrier system based on multiple scenarios using a multi-objective evolutionary algorithm like Non-dominated Sorting based Genetic Algorithm (NSGA-II). Each scenario is represented by a fitness function and represented as a composite function of one or more radio parameters. We model the CR parameter adaptation problem as an unconstrained multi-objective optimization problem and then propose an algorithm to optimize the CR transmission parameters based on NSGA-II. We compute the fitness score by considering multiple scenarios at a time and then evolving the solution until optimal value is reached. The final results are represented as a set of optimal solutions referred as pareto-front for the given scenarios. We performed multi-objective optimization considering two objectives and the best individual fitness values which are obtained after final iteration are reported here as the pareto-front.


Cognitive Radio Modulation Index Cognitive Radio System Software Define Radio Symbol Rate 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Mitola III, J.: An integrated agent architecture for software defined radio. Ph.D. dissertation, Royal Institute of Technology (KTH) (May 2000)Google Scholar
  2. 2.
    Etkin, R., Parekh, A., Tse, D.: Spectrum sharing for unlicensed bands. In: Proc. of IEEE International Symposium on New Frontiers in Dynamic Spectrum Access, vol. 25(3), pp. 517–528 (2007)Google Scholar
  3. 3.
    Akyildiz, I.F., Lee, W.Y., Vuran, M.C., Mohanty, S.: NeXt Generation/Dynamic Spectrum Access/Cognitive Radio Wireless Networks: A Survey. Computer Networks Journal 50, 2127–2159 (2006)MATHCrossRefGoogle Scholar
  4. 4.
    Spectrum Policy Task Force: Report of the Spectrum Efficiency Workgroup (November 2002),
  5. 5.
    Newman, T.R., Barker, B.A., Wyglinski, A.M., Agah, A., Evans, J.B., Minden, G.J.: Cognitive engine implementation for wireless multicarrier transceivers. Wireless Communication and Mobile Computing 7(9), 1129–1142 (2007)CrossRefGoogle Scholar
  6. 6.
    Newmann, T.R.: Ph.D Dissertation on Multiple Objective Fitness Functions for Cognitive Radio Adaptation, pp. 14–69 (2008)Google Scholar
  7. 7.
    Kim, D.I., Hossain, E., Bhargava, V.: Dynamic rate and power adaptation for provisioning class-based QoS in Cellular Multi-rate WCDMA systems. IEEE Transactions on Wireless Communications 3(4), 1590–1601 (2004)CrossRefGoogle Scholar
  8. 8.
    Kose, C., Goeckel, D.: On power adaptation in adaptive signaling systems. IEEE Transactions on Communications 48(11), 1769–1773 (2000)CrossRefGoogle Scholar
  9. 9.
    El-Saleh, A.A., Ismail, M., Ali, M.A.M., Ng, J.: Development of a cognitive radio decision engine using multi-objective hybrid genetic algorithm. In: Proc. of IEEE 9th Malaysia International Conference on Communications (MICC), pp. 343–347 (December 2009)Google Scholar
  10. 10.
    Paris, J., del Carmen Aguayo-Torres, M., Entrambasaguas, J.: Optimum discrete-power adaptive QAM scheme for Rayleigh fading channels. IEEE Transactions on Communications 5(7), 281–283 (2001)Google Scholar
  11. 11.
    Chung, S.T., Goldsmith, A.: Degrees of freedom in adaptive modulation: a unified view. IEEE Transactions on Communications 49(9), 1561–1571 (2001)MATHCrossRefGoogle Scholar
  12. 12.
    Falahati, S., Svensson, A., Ekman, T., Sternad, M.: Adaptive modulation systems for predicted wireless channels. IEEE Transactions on Communications 52(2), 307–316 (2004)CrossRefGoogle Scholar
  13. 13.
    Waheed, M., Cai, A.: Cognitive Radio Parameter Adaptation in Multicarrier Environment. In: Proc. of Fifth International Conference on Wireless and Mobile Communications (ICWMC), pp. 391–395 (2009)Google Scholar
  14. 14.
    Zhao, Z., Xu, S., Zheng, S., Shang, J.: Cognitive radio adaptation using particle swarm optimization. Wireless Communication and Mobile Computing 9(7), 875–881 (2009)CrossRefGoogle Scholar
  15. 15.
    Ma, J., Jiang, H.: Optimal Design of Cognitive Radio Wireless Parameter on Non-dominated Neighbor Distribution Genetic Algorithm. In: Proc. of Eighth IEEE/ACIS International Conference on Computer and Information Science (ICIS 2009), pp. 97–101 (2009)Google Scholar
  16. 16.
    Yong, L., Hong, J., Qing, H.Y.: Design of Cognitive Radio Wiereless Parameters Based on Multi-objective Immune Genetic Algorithm. In: Proc. of WRI International Conference on Communications and Mobile Computing (CMC 2009), vol. 1, pp. 92–96 (2009)Google Scholar
  17. 17.
    Zhang, Y., Zhang, F., He, W.: Adaptive transmission in cognitive radio networks. In: Proc. of Chinese Control and Decision Conference (CCDC 2009), pp. 1951–1953 (2009)Google Scholar
  18. 18.
    Pratap, A., Deb, K., Agarwal, S., Meyarivan, T.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, Springer, Heidelberg (2000)Google Scholar
  19. 19.
    Proakis, J.G.: Digital Communications, 4th edn. McGraw-Hill, New York (2000)Google Scholar

Copyright information

© Springer India Pvt. Ltd. 2012

Authors and Affiliations

  • Deepak K. Tosh
    • 1
  • Siba K. Udgata
    • 1
  • Samrat L. Sabat
    • 2
  1. 1.Department of Computer and Information SciencesUniversity of HyderabadHyderabadIndia
  2. 2.School of PhysicsUniversity of HyderabadHyderabadIndia

Personalised recommendations