Skip to main content
Log in

Scarce-resource capacity sharing in cognitive radio environments: a new game theoretical model

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

The paper proposes a general game theoretical model, called capacity demand game, for treating simultaneous capacity requests in scarce-resource cognitive radio (CR) environments. The approach is that of non-cooperative games describing CR interactions in terms of radio resource access. Experiments reveal stable states (equilibria) that favour an equitable usage of radio resources to the benefit of all participants. Several equilibria are detected and discussed: Nash (NE), Pareto, joint Nash–Pareto, and Lorenz equilibrium.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Neel, J., Buehrer, R. M., Reed, B. H., & Gilles, R. P. (August 2002). Game theoretic analysis of a network of cognitive radios. In Circuits and systems, 2002. MWSCAS-2002. The 2002 45th midwest symposium on (Vol. 3, pp. III-409–III-412).

  2. MItola, J. (2000). Cognitive radio: An integrated agent architecture for software defined radio. PhD thesis, Royal Institute of Technology (KTH), Stockholm, Sweden.

  3. Doyle, L. E. (2009). Essentials of cognitive radio. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  4. Nokia. (2011). Nokia Siemens Networks White paper, Liquid radio. Let traffic waves flow most efficiently.

  5. Alcatel. (2011). Alcatel Lucent Technology White paper, Light Radio.

  6. Ofcom. (2014). Meeting the demands for wireless services: Ofcom publishes spectrum blueprint for the next decade.

  7. Ericsson. (February 2011). More than 50 billion connected devices.

  8. Costa, G. W. O., Cattoni, A. F., Kovacs, I. Z., & Mogensen, P. E. (2012). A fully distributed method for dynamic spectrum sharing in femtocells. In Wireless communications and networking conference workshops (WCNCW), 2012 IEEE (pp. 87–92)

  9. DaCosta, O. G. W., Cattoni, A. F., Kovacs, I. Z., & Mogensen, P. (2010). Scalable spectrum sharing mechanism for local area networks deployment. IEEE Transactions on Vehicular Technology, 59(4), 1630–1645.

    Article  Google Scholar 

  10. Sodagari, S., & Bilen, S. G. (2011). On cost-sharing mechanisms in cognitive radionetworks. European Transactions on Telecommunications, 22, 515–521.

    Article  Google Scholar 

  11. Wang, B., Yongle, W., & Liu, K. J. R. (2010). Game theory for cognitive radio networks: An overview. Computer Networks, 54(14), 2537–2561.

    Article  Google Scholar 

  12. D’Oro, S., Mertikopoulos, P., Moustakas, A. L., & Palazzo, S. (2015). Interference-based pricing for opportunistic multicarrier cognitive radio systems. IEEE Transactions on Wireless Communications, 14(12), 6536–6549.

    Article  Google Scholar 

  13. Bacci, G., Lasaulce, S., Saad, W., & Luca, S. (2014). The game theory side of signal processing. In IEEE signal processing magazine, special issue on digital right management (pp. 1–40).

  14. Huang, J. W., & Krishnamurthy, V. (2009). Game theoretic issues in cognitive radio systems. Journal of Communications, 4, 790–802.

    Google Scholar 

  15. MacKenzie, A. B., & Wicker, S. B. (2001). Game theory in communications: Motivation, explanation, and application to power control. In Global telecommunications conference, 2001. GLOBECOM ’01. IEEE (Vol. 2, pp. 821–826).

  16. Maskery, M., Krishnamurthy, V., & Qing, Z. (2007). Game theoretic learning and pricing for dynamic spectrum access in cognitive radio. In E. Hossain & V. Bhargava (Eds.), Cognitive wireless communication networks (pp. 303–325). US: Springer.

  17. Neel, J. O. (2006). Analysis and design of cognitive radio networks and distributed radio resource management algorithms. PhD thesis, Faculty of the Virginia Polytechnic Institute and State University.

  18. Niyato, D., & Ekram, H. (2007). Microeconomic models for dynamic spectrum management in cognitive radio networks. In E. Hossain & V. Bhargava (Eds.), Cognitive wireless communication networks (pp. 391–423). US: Springer.

  19. Wang, B., Yongle, W., & Liu, K. J. R. (2010). Game theory for cognitive radio networks: An overview. Computer Networks, 54(14), 2537–2561.

    Article  Google Scholar 

  20. Xiao, Y., Bi, G., Niyato, D., & DaSilva, L. A. (2012). A hierarchical game theoretic framework for cognitive radio networks. IEEE Journal on Selected Areas in Communications, 30(10), 2053–2069.

    Article  Google Scholar 

  21. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

    Article  Google Scholar 

  22. Nash, J. F. (1951). Non-cooperative games. Annals of Mathematics, 54, 286–295.

    Article  Google Scholar 

  23. Cremene, L. C. C., & Dumitrescu, D. (2014). A relevant equilibrium in open spectrum sharing: Lorenz equilibrium in discrete games. In International conference on next generation wired/wireless networking (pp. 356–363). Springer.

  24. Kostreva, M. M., & Wodzimierz, O. (1999). Linear optimization with multiple equitable criteria. RAIRO-Operations Research, 33, 275–297.

    Article  Google Scholar 

  25. Nagy, R., Dumitrescu, D., & Lung, R. I. (September 2011). Lorenz equilibrium: Concept and evolutionary detection. In Symbolic and numeric algorithms for scientific computing (SYNASC), 2011 13th international symposium on (pp. 408–412).

  26. Aumann, R. J., & Hart, S. (1992). Handbook of game theory with economic applications, volume 1 of Handbook of game theory with economic applications. Elsevier.

  27. Osborne, M. J. (2009). An introduction to game theory. Oxford: Oxford University Press.

    Google Scholar 

  28. Etkin, R., Parekh, A., & Tse, D. (2007). Spectrum sharing for unlicensed bands. IEEE Journal on Selected Areas in Communications, 25(3), 517–528.

    Article  Google Scholar 

  29. Fudenberg, D., & Tirole, J. (1983). Multiple Nash equilibria, focal points, and Pareto optimality, game theory. Cambridge: MIT Press.

    Google Scholar 

  30. Nagy, R., Suciu, M. A., & Dumitrescu, D. (2012). Lorenz equilibrium: Equitability in non-cooperative games. In GECCO (pp. 489–496).

  31. Nash, J. F, Jr. (1950). The bargaining problem. Econometrica, 18(2), 155–162.

    Article  Google Scholar 

  32. Dumitrescu, D., Lung, R. I., & Mihoc, T. D. (2009). Evolutionary equilibria detection in non-cooperative games. In Applications of evolutionary computing, Volume 5484 of Lecture Notes in Computer Science (pp. 253–262). Berlin: Springer.

  33. Bertrand, J. (1883). Book review of theorie mathematique de la richesse sociale and of recherches sur les principles mathematiques de la theorie des richesses. Journal de Savants, 67, 499–508.

    Google Scholar 

  34. Hotelling, H. (1929). Stability in competition. The Economic Journal, 39, 41–57.

    Article  Google Scholar 

  35. Nash, J. F. (1953). Two-person cooperative games. Econometrica, 21, 128–140.

    Article  Google Scholar 

  36. Coello, C. A. C. (1999). A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowledge and Information systems, 1(3), 269–308.

    Article  Google Scholar 

  37. Lung, R. I., & Dumitrescu, D. (2008). Computing Nash equilibria by means of evolutionary computation. International Journal of Computers, Communications and Control, 3, 364–368.

    Google Scholar 

  38. Dumitrescu, D., Lung, R. I., & Mihoc, T. D. (2009). Generative relations for evolutionary equilibria detection. In Proceedings of the 11th annual conference on genetic and evolutionary computation (pp. 1507–1512).

Download references

Acknowledgements

This paper was partially supported by The Technical University of Cluj-Napoca internal competition Project 24311/2013-2014.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marcel Cremene.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cremene, L., Gaskó, N., Cremene, M. et al. Scarce-resource capacity sharing in cognitive radio environments: a new game theoretical model. Telecommun Syst 66, 331–342 (2017). https://doi.org/10.1007/s11235-017-0292-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-017-0292-5

Keywords

Navigation