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Coalition Game Theory in Cognitive Mobile Radio Networks

  • Pablo PalaciosEmail author
  • Carlos SaavedraEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 895)

Abstract

In this work, the impact and performance of the Coalition Game Theory applied directly to the detection and decision stages of a Cognitive Radio (CR) system is evaluated. The performance of the Coalitional Game was analyzed in terms of the Probability of detection (\({P_d}\)) and Probability of false alarm (\({P_{fa}}\)) versus number of secondary users (SUs). In addition, the detection accuracy and simulation time versus SU were analyzed in a structured network adapted for WiFi and LTE technologies with cognitive parameters. The results were compared using simulation scenarios to obtain data using the theoretical Non-cooperative decision method and the theoretical Centralized decision method. The evaluated system outperformed the other methods in terms of \({P_d}\), \({P_{fa}}\), detection accuracy and simulation time.

Keywords

Cognitive mobile radio networks Probability of detection (\({P_d}\)Probability of false alarm (\({P_{fa}}\)Coalition game theory Spectrum decision Spectrum sensing 

References

  1. 1.
    Ramani, V., Sharma, S.K.: Cognitive radios: a survey on spectrum sensing, security and spectrum handoff. Chin. Commun. 14(11), 185–208 (2017)CrossRefGoogle Scholar
  2. 2.
    Hu, F., Chen, B., Zhu, K.: Full spectrum sharing in cognitive radio networks toward 5G: a survey. IEEE Access PP(99), 1 (2018)Google Scholar
  3. 3.
    Luís, M., Oliveira, R., Dinis, R., Bernardo, L.: RF-spectrum opportunities for cognitive radio networks operating over GSM channels. IEEE Trans. Cogn. Commun. Netw. 3(4), 731–739 (2017)CrossRefGoogle Scholar
  4. 4.
    Wang, J., Feng, S., Wu, Q., Zheng, X., Xu, Y.: Hierarchical cognition cycle for cognitive radio networks. Chin. Commun. 12(1), 108–121 (2015)CrossRefGoogle Scholar
  5. 5.
    Saad, W., Han, Z., Basar, T., Debbah, M., Hjorungnes, A.: Coalition formation games for collaborative spectrum sensing. IEEE Trans. Veh. Technol. 60(1), 276–297 (2011)CrossRefGoogle Scholar
  6. 6.
    Wang, B., Liu, K.J.R., Clancy, T.C.: Evolutionary cooperative spectrum sensing game: how to collaborate? IEEE Trans. Commun. 58(3), 890–900 (2010)CrossRefGoogle Scholar
  7. 7.
    Niyato, D., Hossain, E.: A game-theoretic approach to competitive spectrum sharing in cognitive radio networks. In: 2007 IEEE Wireless Communications and Networking Conference, Kowloon, pp. 16–20 (2007)Google Scholar
  8. 8.
    Peh, E.C.Y., Liang, Y.C., Guan, Y.L., Zeng, Y.: Cooperative spectrum sensing in cognitive radio networks with weighted decision fusion schemes. IEEE Trans. Wirel. Commun. 9(12), 3838–3847 (2010)CrossRefGoogle Scholar
  9. 9.
    Chaudhari, S., Lunden, J., Koivunen, V., Poor, H.V.: Cooperative sensing with imperfect reporting channels: hard decisions or soft decisions? IEEE Trans. Sig. Process. 60(1), 18–28 (2012)MathSciNetCrossRefGoogle Scholar
  10. 10.
  11. 11.
    Li, H., et al.: Utility-based cooperative spectrum sensing scheduling in cognitive radio networks. IEEE Trans. Veh. Technol. 66(1), 645–655 (2017)Google Scholar
  12. 12.
    Abuzainab, N., Vinnakota, S.R., Touati, C.: Coalition formation game for cooperative cognitive radio using Gibbs sampling. In: 2015 IEEE Wireless Communications and Networking Conference (WCNC), New Orleans, LA, pp. 937–942 (2015)Google Scholar
  13. 13.
    Fenila Janet, M., Lavanya, S., Bhagyaveni, M.A.: Performance analysis of cooperative spectrum sensing in cognitive radio using game theory. In: 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, pp. 2061–2065 (2016)Google Scholar
  14. 14.
    Hyder, C.S., Xiao, L.: Cooperative routing via overlapping coalition formation game in cognitive radio networks. In: 2016 25th International Conference on Computer Communication and Networks (ICCCN), Waikoloa, HI, pp. 1–6 (2016)Google Scholar
  15. 15.
    Palacios, P., Castro, A., Azurdia-Meza, C., Estevez, C.: Signal detection methods in cognitive mobile radio networks: a performance comparison. In: IEEE Latin-American Conference on Communications (LATINCOM), 2017 Workshop, Guatemala (2017)Google Scholar
  16. 16.
    Palacios, P., Castro, A., Azurdia-Meza, C., Estevez, C.: SVD detection analysis in cognitive mobile radio networks. In: 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), Milan, pp. 222–224 (2017)Google Scholar
  17. 17.
    Mathur, S., Sankaranarayanan, L., Mandayam, N.: Coalitions in cooperative wireless networks. IEEE J. Sel. Areas Commun. 26, 1104–1115 (2008)CrossRefGoogle Scholar
  18. 18.
    Han, Z., Liu, K.J.: Resource allocation for wireless networks: basics, techniques, and applications. Cambridge University Press, Cambridge (2008)CrossRefGoogle Scholar
  19. 19.
    Shiryaev, A.N.: On optimum methods in quickest detection problems. Theory Probab. Appl. 8(1), 22–46 (1963)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Saad, W., Han, Z., Debbah, M., Hjørungnes, A., Başar, T.: Coalitional games for distributed collaborative spectrum sensing in cognitive radio networks. In: Proceedings of IEEE INFOCOM, Rio de Janeiro, Brazil, April 2009Google Scholar
  21. 21.
    Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)Google Scholar
  22. 22.
    Owen, G.: Game Theory, 3rd edn. Academic, London (1995)zbMATHGoogle Scholar
  23. 23.
    Czajkowski, K., Fitzgerald, S., Foster, I., Kesselman, C.: Grid information services for distributed resource sharing. In: 10th IEEE International Symposium on High Performance Distributed Computing, pp. 181–184. IEEE Press, New York (2001)Google Scholar
  24. 24.
    Foster, I., Kesselman, C., Nick, J., Tuecke, S.: The physiology of the grid: an open grid services architecture for distributed systems integration. Technical report, Global Grid Forum (2002)Google Scholar
  25. 25.
    National Center for Biotechnology Information. http://www.ncbi.nlm.nih.gov
  26. 26.
    Ghasemi, A., Sousa, E.S.: Collaborative spectrum sensing for opportunistic access in fading environments. In: IEEE Symposium New Frontiers in Dynamic Spectrum Access Networks, Baltimore, USA, pp. 131–136, November 2005Google Scholar
  27. 27.
    Visotsky, E., Kuffner, S., Peterson, R.: On collaborative detection of TV transmissions in support of dynamic spectrum sensing. In: IEEE Symposium New Frontiers in Dynamic Spectrum Access Networks, Baltimore, USA, pp. 338–356, November 2005Google Scholar
  28. 28.
    Niyato, D., Hossein, E., Han, Z.: Dynamic Spectrum Access in Cognitive Radio Networks. Cambridge University Press, Cambridge (2009)Google Scholar
  29. 29.
    Myerson, R.: Graphs and cooperation in games. Math. Oper. Res. 2, 225–229 (1977)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Saad, W., Han, Z., Debbah, M., Hjørungnes, A.: Network formation games for distributed uplink tree construction in IEEE 802.16j networks. In: Proceedings of IEEE Global Communication Conference, pp. 1–5, New Orleans, LA, December 2008Google Scholar
  31. 31.
    Alfonso, U.M., Carla, M.V.: Modelado y simulación de eventos discretos. Editorial UNED (2013)Google Scholar
  32. 32.
    Ramírez, I.C., Barrera, C.J., Correa, J.C.: Efecto del tamañoo de muestra y el número de réplicas bootstrap. Ingeniería y Competitividad 15(1), 93–101 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Departamento de Redes y TelecomunicacionesUniversidad De Las AméricasQuitoEcuador
  2. 2.Department of Electronics and Radio EngineeringKyung Hee UniversityYonginSouth Korea

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