Skip to main content
SpringerLink
Log in

Coalition Game Theory in Cognitive Mobile Radio Networks

  • Conference paper
  • First Online:
Technology Trends (CITT 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 895))

Included in the following conference series:

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.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ramani, V., Sharma, S.K.: Cognitive radios: a survey on spectrum sensing, security and spectrum handoff. Chin. Commun. 14(11), 185–208 (2017)

    Google Scholar 

  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. 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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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. 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)

    Google Scholar 

  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)

    Google Scholar 

  10. ns-3 Model Library, Release ns-3.23. https://www.nsnam.org/docs/release/3.23/models/ns-3-model-library.pdf

  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. 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. 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. 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. 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. 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. Mathur, S., Sankaranarayanan, L., Mandayam, N.: Coalitions in cooperative wireless networks. IEEE J. Sel. Areas Commun. 26, 1104–1115 (2008)

    Google Scholar 

  18. Han, Z., Liu, K.J.: Resource allocation for wireless networks: basics, techniques, and applications. Cambridge University Press, Cambridge (2008)

    Google Scholar 

  19. Shiryaev, A.N.: On optimum methods in quickest detection problems. Theory Probab. Appl. 8(1), 22–46 (1963)

    Google Scholar 

  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 2009

    Google Scholar 

  21. Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  22. Owen, G.: Game Theory, 3rd edn. Academic, London (1995)

    Google Scholar 

  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. 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. National Center for Biotechnology Information. http://www.ncbi.nlm.nih.gov

  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 2005

    Google Scholar 

  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 2005

    Google Scholar 

  28. Niyato, D., Hossein, E., Han, Z.: Dynamic Spectrum Access in Cognitive Radio Networks. Cambridge University Press, Cambridge (2009)

    Google Scholar 

  29. Myerson, R.: Graphs and cooperation in games. Math. Oper. Res. 2, 225–229 (1977)

    Google Scholar 

  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 2008

    Google Scholar 

  31. Alfonso, U.M., Carla, M.V.: Modelado y simulación de eventos discretos. Editorial UNED (2013)

    Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

  1. Departamento de Redes y Telecomunicaciones, Universidad De Las Américas, Quito, Ecuador

    Pablo Palacios

  2. Department of Electronics and Radio Engineering, Kyung Hee University, Yongin, 446-701, South Korea

    Carlos Saavedra

Authors
  1. Pablo Palacios
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carlos Saavedra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Pablo Palacios or Carlos Saavedra .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, Noord-Brabant, The Netherlands

    Miguel Botto-Tobar

  2. Computer Science, Politecnica Salesiana University, Cuenca, Ecuador

    Guillermo Pizarro

  3. Facultad de Ingenieria, University of Cuenca, Cuenca, Ecuador

    Miguel Zúñiga-Prieto

  4. Universidad Estatal de Milagro, San Francisco de Milagro, Ecuador

    Mayra D’Armas

  5. Universidad Técnica de Babahoyo, Babahoyo, Ecuador

    Miguel Zúñiga Sánchez

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Palacios, P., Saavedra, C. (2019). Coalition Game Theory in Cognitive Mobile Radio Networks. In: Botto-Tobar, M., Pizarro, G., Zúñiga-Prieto, M., D’Armas, M., Zúñiga Sánchez, M. (eds) Technology Trends. CITT 2018. Communications in Computer and Information Science, vol 895. Springer, Cham. https://doi.org/10.1007/978-3-030-05532-5_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-05532-5_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05531-8

  • Online ISBN: 978-3-030-05532-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation