Multi Objective Optimization of Expense and Revenue in a Cognitive Radio Network Using NSGA-II

  • Subhasree Bhattacharjee
  • Suman Bhattacharjee
  • Roukna Sengupta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)


In this paper we consider an auction framework of cognitive radio network comprises of primary and secondary users (SU). The spectrum is divided into channels using frequency division multiple access (FDMA). Primary users have license to use the channels. When the channels are idle, primary users lease the vacant spectrum for monetary gain. Cognitive users or SUs bid for the channels. The purchaser who provides highest bid value is selected by primary user. Our objective is to maximize the revenue earned by primary users and to minimize the expense given by the secondary users. The problem is bi-objective and both the objectives are conflicting. Using Non dominated sorting genetic algorithm II; we solve both the objectives of primary and secondary users. The algorithm solves the problem well and find optimize values of both expense and revenue.


Multi Objective Optimization Primary User Pareto Front Secondary User Cognitive Radio Network 
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  1. 1.
    Zheng, H., Peng, C.: Collaboration and fairness in opportunistic spectrum access. In: Proc. of IEEE International Conference on Communications (ICC 2005), Seoul, pp. 3132–3136 (May 2005)Google Scholar
  2. 2.
    Keshavamurthy, S., Chandra, K.: Multiplexing analysis for spectrum sharing. In: Proc. of IEEE MILCOMM 2006, Washington D.C., pp. 1–7 (October 2006)Google Scholar
  3. 3.
    Niyato, D., Hossain, E.: Spectrum trading in cognitive radio networks:a market-equilibrium-basedapproach. IEEE Wireless Communications 15(6), 71–80 (2008)CrossRefGoogle Scholar
  4. 4.
    Beibei, W., Yongle, W.: Game theoretical mechanism design methods. IEEE Signal Processing Magazine 25(6), 74–84 (2008)CrossRefGoogle Scholar
  5. 5.
    Coello, C.A., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation 3, 256–279 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Subhasree Bhattacharjee
    • 1
  • Suman Bhattacharjee
    • 2
  • Roukna Sengupta
    • 3
  1. 1.Dept. of CANarula Institute of TechnologyKolkataIndia
  2. 2.IBM INDIA Pvt. Ltd.KolkataIndia
  3. 3.Dept. of CSERCC Institute of Information TechnologyKolkataIndia

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