Improved Quantum Genetic Algorithm for Competitive Spectrum Sharing in Cognitive Radios

  • Fei Li
  • Dongpo Zhu
  • Feng Tian
  • Haibo Li
Conference paper


This chapter investigates a kind of whole new approach to spectrum sharing in cognitive radio (CR) system, quantum-inspired approach. First, we improved Quantum Genetic Algorithm (QGA) by adding quantum crossover operator to overcome the shortcomings of the original QGA, easily falling into a local extremum when used to optimize the continuous functions with many extrema. We then propose a novel spectrum sharing scheme in noncooperative game for CR system based on our improved QGA. We use QGA as competitive strategies and provide simulation results that confirm the proposed schemes have better convergence rate and higher sum capacity than genetic algorithm (GA)-based scheme, namely at most 1 bit/s/Hz increase in capacity. The results demonstrate the effectiveness and the applicability of QGA in spectrum sharing in CR system.


Quantum genetic algorithm Spectrum sharing Cognitive radio Game theory 



This work was supported in part by the Jiangsu Government Scholarship for Overseas Studies and the National Natural Science Foundation of China (No. 61001077).


  1. 1.
    Han, K.H., Kim, J.H.: Genetic Quantum Algorithm and its Application to Combinatorial Optimization Problem. In: Proc. of 2000 IEEE Congress on Evolutionary Computation, pp.1354–1360. IEEE Press, Piscataway (2000)Google Scholar
  2. 2.
    Nielsen, M.A., Chuang, I.L.: Quantum Computation and Quantum Information, Cambridge University Press (2000)Google Scholar
  3. 3.
    Han, K.H., Kim, J.H.: Quantum-Inspired Evolutionary Algorithm for a Class of Combinatorial Optimization. . IEEE Transactions on Evolutionary Computation, Vol. 6 (6), 580–593 (2002)CrossRefGoogle Scholar
  4. 4.
    Yang, J.A., Zhuang. Z.Q.: Research of Quantum Genetic Algorithm and its Application in Blind Source Separation. Journal of Electronics (China), Vol. 20(1), 62–68 (2003)CrossRefGoogle Scholar
  5. 5.
    Xiong, Y., Chen, H.H., Miao, F.Y., Wang, X.F.: A Quantum Genetic Algorithm to Solve Combinatorial Optimization Problem. J. ACTA ELECTRONICA SINICA. Vol. 32(11), 1855–1858 (2004)Google Scholar
  6. 6.
    Li, F., Hong,, L., Zheng, B.Y.: Quantum Genetic Algorithm and its Application to Multi-user Detection In: Proc. of 2008 IEEE International Conference on signal Processing, pp.1951–1954. IEEE Press, Beijing (2008)Google Scholar
  7. 7.
    Mitola, J.: Cognitive Radio for Flexible Mobile Multimedia Communications. In: 1999 IEEE International Workshop on Mobile Multimedia Communications (MoMuC’99), pp. 3–10. IEEE Press, San Diego (1999)Google Scholar
  8. 8.
    Laufer, A., Leshem, A.: Distributed Coordination of Spectrum and the Prisoner’s Dilemma. In: Proc. of DySPAN’2005, pp. 94–100. Maryland (2005)Google Scholar
  9. 9.
    Clemens, N., Rose, C.: Intelligent Power Allocation Strategies in an Unlicensed Spectrum. In: Proc. of DySPAN’2005, pp.37–42. Maryland (2005)Google Scholar
  10. 10.
    Niyato, D., Hossain, E.: Competitive Spectrum Sharing in Cognitive Radio Networks: A Dynamic Game Approach. IEEE Transactions on Wireless Communications, Vol. 7 (7), 2651–2660 (2008)Google Scholar
  11. 11.
    Tian, F. Yang, Z.: Spectrum Sharing in Iterated Prisoner’s Dilemma game based on Evolutionary Strategies for Cognitive Radios. Journal of Electronics (China), Vol. 26 (5), 588–599 (2009)CrossRefGoogle Scholar
  12. 12.
    Molga, M. Smutnicki, C.: Test functions for optimization needs, (2005)

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Institute of Signal Processing and Transmission, Key Laboratory of Broadband Wireless Communication and Sensor Network Technology, Ministry of EducationNanjing University of Posts and TelecommunicationsNanjingChina
  2. 2.Department of Applied Physics and ElectronicsUmeå UniversityUmeåSweden

Personalised recommendations