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.
Similar content being viewed by others
References
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).
MItola, J. (2000). Cognitive radio: An integrated agent architecture for software defined radio. PhD thesis, Royal Institute of Technology (KTH), Stockholm, Sweden.
Doyle, L. E. (2009). Essentials of cognitive radio. Cambridge: Cambridge University Press.
Nokia. (2011). Nokia Siemens Networks White paper, Liquid radio. Let traffic waves flow most efficiently.
Alcatel. (2011). Alcatel Lucent Technology White paper, Light Radio.
Ofcom. (2014). Meeting the demands for wireless services: Ofcom publishes spectrum blueprint for the next decade.
Ericsson. (February 2011). More than 50 billion connected devices.
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)
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.
Sodagari, S., & Bilen, S. G. (2011). On cost-sharing mechanisms in cognitive radionetworks. European Transactions on Telecommunications, 22, 515–521.
Wang, B., Yongle, W., & Liu, K. J. R. (2010). Game theory for cognitive radio networks: An overview. Computer Networks, 54(14), 2537–2561.
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.
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).
Huang, J. W., & Krishnamurthy, V. (2009). Game theoretic issues in cognitive radio systems. Journal of Communications, 4, 790–802.
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).
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.
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.
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.
Wang, B., Yongle, W., & Liu, K. J. R. (2010). Game theory for cognitive radio networks: An overview. Computer Networks, 54(14), 2537–2561.
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.
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.
Nash, J. F. (1951). Non-cooperative games. Annals of Mathematics, 54, 286–295.
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.
Kostreva, M. M., & Wodzimierz, O. (1999). Linear optimization with multiple equitable criteria. RAIRO-Operations Research, 33, 275–297.
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).
Aumann, R. J., & Hart, S. (1992). Handbook of game theory with economic applications, volume 1 of Handbook of game theory with economic applications. Elsevier.
Osborne, M. J. (2009). An introduction to game theory. Oxford: Oxford University Press.
Etkin, R., Parekh, A., & Tse, D. (2007). Spectrum sharing for unlicensed bands. IEEE Journal on Selected Areas in Communications, 25(3), 517–528.
Fudenberg, D., & Tirole, J. (1983). Multiple Nash equilibria, focal points, and Pareto optimality, game theory. Cambridge: MIT Press.
Nagy, R., Suciu, M. A., & Dumitrescu, D. (2012). Lorenz equilibrium: Equitability in non-cooperative games. In GECCO (pp. 489–496).
Nash, J. F, Jr. (1950). The bargaining problem. Econometrica, 18(2), 155–162.
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.
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.
Hotelling, H. (1929). Stability in competition. The Economic Journal, 39, 41–57.
Nash, J. F. (1953). Two-person cooperative games. Econometrica, 21, 128–140.
Coello, C. A. C. (1999). A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowledge and Information systems, 1(3), 269–308.
Lung, R. I., & Dumitrescu, D. (2008). Computing Nash equilibria by means of evolutionary computation. International Journal of Computers, Communications and Control, 3, 364–368.
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).
Acknowledgements
This paper was partially supported by The Technical University of Cluj-Napoca internal competition Project 24311/2013-2014.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11235-017-0292-5