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Wireless Networks

, Volume 25, Issue 4, pp 1777–1789 | Cite as

An optimal transmission strategy in zero-sum matrix games under intelligent jamming attacks

  • Senthuran ArunthavanathanEmail author
  • Leonardo Goratti
  • Lorenzo Maggi
  • Francesco de Pellegrini
  • Sithamparanathan Kandeepan
  • Sam Reisenfield
Article
  • 132 Downloads

Abstract

Cognitive radio networks are more susceptible to jamming attacks due to the nature of unlicensed users accessing the spectrum by performing dynamic spectrum access. In such a context, a natural concern for operators is the resilience of the system. We model such a scenario as one of adversity in the system consisting of a single legitimate (LU) pair and malicious user (MU). The aim of the LU is to maximize throughput of transmissions, while the MU is to minimize the throughput of the LU completely. We present the achievable transmission rate of the LU pair under jamming attacks taking into account mainly on the transmission power per channel. Furthermore, we embed our utility function in a zero-sum matrix game and extend this by employing a fictitious play when both players learn each other’s strategy over time, e.g., such an equilibrium becomes the system’s global operating point. We further extend this to a reinforcement learning (RL) approach, where the LU is given the advantage of incorporating RL methods to maximize its throughput for fixed jamming strategies.

Keywords

Anti-jamming game Zero-sum games Reinforcement learning Fictitious play 

Notes

Acknowledgements

The research of S. Arunthavanathan, L. Goratti, and S. Kandeepan, leading to these results, has received partial funding from the EC 7 Framework Programme (FP7-2011-8) under the Grant Agreement FP7-ICT-318632. The work of F. De Pellegrini and L. Maggi has been partially supported by the European Commission within the framework of the CONGAS project FP7-ICT-2011-8-317672, see www.congas-project.eu.

References

  1. 1.
    Kandeepan, S., Gomez, K., Gorratti, L., Rasheed, T., & Baldini, G. (2016). Power controlling for device to device transmissions in aerial access networks. In IEEE ATC, pp. 48–53.Google Scholar
  2. 2.
    Al-Hourani, A., Kandeepan, S., & Hossain, E. (2016). Relay-assisted device-to-device communication: a stochastic analysis of energy saving. In: IEEE TMC.Google Scholar
  3. 3.
    Khare, A., Saxena, M., Thakur, R. S., & Chourasia, K. (2013). Attacks and preventions of cognitive networks: A survey. IJARCET, 2(3), 1002–1006.Google Scholar
  4. 4.
    Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas of Communications, 23(2), 201–220.CrossRefGoogle Scholar
  5. 5.
    Mitola, J., & Maguire, G. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.CrossRefGoogle Scholar
  6. 6.
    Al-Hourani, A., & Kandeepan, S. (Dec. 2013). Cognitive relay nodes for airborne LTE emergency networks. In IEEE ICSPCS, pp. 1–9.Google Scholar
  7. 7.
    Doumi, T., Dolan, M. F., Tatesh, S., Casati, A., Tsirtsis, G., Anchan, K., et al. (2013). LTE for public safety networks. IEEE Wireless Communications, 51(2), 106–112.Google Scholar
  8. 8.
    Sesia, S., Toufik, I., & Baker, M. (2009). The UMTS long term evolution from theory to practice (2nd ed.). Hoboken: Wiley.Google Scholar
  9. 9.
    Fodor, G., Dalman, E., Mildh, G., Parkvall, S., Reider, N., Miklós, G., et al. (2012). Design aspect of network assisted device-to-device communications. IEEE Wireless Communication, 50(3), 170–177.Google Scholar
  10. 10.
    Doppler, K., Rinne, M., Wijting, C., Ribeiro, C. B., & Hugl, K. (2012). Device-to-device communication as an uderlay to LTE-advanced networks. IEEE Wireless Communications Magazine, 47(12), 42–49.CrossRefGoogle Scholar
  11. 11.
    Aerial Base Stations with Opportunistic Links for Unexpected and Temporary Events (ABSOLUTE). EU FP7 Integrated Project. http://www.absolute-project.eu/.
  12. 12.
    Wang, B., Wu, Y., & Ray Liu, K. J. (2009). Optimal power allocation strategy against jamming attacks using the Colonel Blotto game. In Proceedings of IEEE GLOBECOM, pp. 1–5.Google Scholar
  13. 13.
    Wu, Y., Wang, B., Ray Liu, K. J., & Clancy, T. C. (2012). Anti-jamming games in multi-channel cognitive radio networks. IEEE Communications, 30(1), 1–12.Google Scholar
  14. 14.
    Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge: MIT Press.zbMATHGoogle Scholar
  15. 15.
    Chen, C., Song, M., Xin, C., & Backens, J. (2013). A game-theoretical anti-jamming scheme for cognitive radio networks. IEEE Networks, 27(3), 22–27.CrossRefGoogle Scholar
  16. 16.
    Li, Y., Yang, R., & Ye, F. (2010). Non-cooperative spectrum allocation based on game theory in cognitive radio networks. In IEEE BIC-TA, pp. 1134–1137.Google Scholar
  17. 17.
    Singh, S., & Trivedi, A. (Sept. 2012). Anti-jamming in cognitive radio networks using reinforcement learning algorithms. In IEEE WOCN, pp. 1–5.Google Scholar
  18. 18.
    Sodagari, S., & Charles Clancy, T. (2011). An anti-jamming strategy for channel access in cognitive radio networks. Berlin: Springer.CrossRefGoogle Scholar
  19. 19.
    Gwon, Y., Dastangooand Carl Fossa, S., & Kung, H. T. (Feb. 2013). Competing mobile network game: Embracing antijamming and jamming strategies with reinforcement learning. In IEEE conference on communications and network security (CNS).Google Scholar
  20. 20.
    Goratti, L., Gomez, K. M., Fedrizzi, R., & Rasheed, T. (Dec. 2013). A novel device-to-device communication protocol for public safety applications. In IEEE Globecom 2013 D2D Workshop, Atlanta.Google Scholar
  21. 21.
    Firouzbakht, K., Noubir, G., & Salehi, M. (2012). On the capacity of rate-adaptive packetized wireless communication links under jamming. In Proceedings of the ACM WISEC conference.Google Scholar
  22. 22.
    Hanawal, M. K., Abdel-Rahman, M. J., & Krunz, M. (2014). Game theoretic anti-jamming dynamic frequency hopping and rate adaptation in wireless systems. In Proceedings of the WiOpt conference.Google Scholar
  23. 23.
    Hanawal, M. K., Abdel-Rahman, M. J., & Krunz, M. (2015). Joint adaptation of frequency hopping and transmission rate for anti-jamming wireless systems. IEEE Transactions on Mobile Computing.Google Scholar
  24. 24.
    Xiao, L., Li, Y., & Zhao, Y. (2015). Power control with reinforcement learning in cooperative cognitive radio networks against jamming. Springer Journal of Supercomputing, 71(9), 3237–3257.CrossRefGoogle Scholar
  25. 25.
    Xiao, L., Liu, J., Li, Q., Mandayam, N., & Poor, V. H. (2015). User-centric view of jamming games in cognitive radio networks. IEEE Transactions on Information Forensics & Security, 10(12), 2578–2590.CrossRefGoogle Scholar
  26. 26.
    Zhou, B., Hu, H., Huang, S. Q., & Chen, H. H. (2013). Intracluster device-to-device relay algorithm with optimal resource utilization. IEEE Transactions on Vehicular Technology, 62(5), 2315–2326.CrossRefGoogle Scholar
  27. 27.
    Raghothaman, B., Deng, E., Pragada, R., Sternberg, G., Deng, T., & Vanganuru, K. (2013). Computing, networking and communications (ICNC). In 2013 international conference on architecture and protocols for LTE-based device to device communication, pp. 895–899.Google Scholar
  28. 28.
    Arunthavanathan, S., Goratti, L., Maggi, L., de Pellegrini, F., & Kandeepan, S. (Jun. 2014). On the achievable rate in a D2D cognitive secondary network under jamming attacks. In IEEE CrownComm, pp. 39–44.Google Scholar
  29. 29.
    Tague, P., Li, M., & Poovendran, R. (2009). Mitigation of control channel jamming under node capture attacks. IEEE Transactions on Mobile Computing, 8(9), 1221–1234.CrossRefGoogle Scholar
  30. 30.
    Garnaev, A., & Trappe, W. (2015). One-time spectrum coexistence in dynamic spectrum access when the secondary user may be malicious. IEEE Transactions on Information Forensics and Security, 10(5), 1064–1075.CrossRefGoogle Scholar
  31. 31.
    Arunthavanathan, S., Kandeepan, S., & Evans, R. (Sept. 2013). Spectrum sensing and detection of incumbent-UEs in secondary-LTE based aerial-terrestrial networks for disaster recovery. In IEEE CAMAD, pp. 201–206.Google Scholar
  32. 32.
    Altman, E., Avrachenkov, K., & Garnaev, A. (2010). Fair resource allocation in wireless networks in the presence of a jammer. Performance Evaluation, 67(4), 338–349.CrossRefGoogle Scholar
  33. 33.
    Von Neumann, J. (1928). Zur theorie der gesellschaftsspiele. Mathematische Annalen, 100, 295–320.MathSciNetCrossRefzbMATHGoogle Scholar
  34. 34.
    Myerson, R. B. (1997). Game theory: Analysis of conflict. Cambridge: Harvard University Press.zbMATHGoogle Scholar
  35. 35.
    Brown, G. W. (1951). Iterative solutions of games by fictitious play in activity analysis of production and allocation. In T. C. Koopmans (Ed.), Activity analysis of production and allocation (pp. 374–376). Hoboken: Wiley.Google Scholar
  36. 36.
    Brown, G. W., & von Neumann, J. (1950). Solutions of games by differential equations: Contributions to the theory of games I: Annals of mathemathical studies (Vol. 24, pp. 73–79). Princeton: Princeton University Press.Google Scholar
  37. 37.
    Robinson, J. (1951). An iterative method of solving a game. Annals of Mathematical Statistics, 54, 296–301.MathSciNetCrossRefzbMATHGoogle Scholar
  38. 38.
    Arunthavanathan, S., Kandeepan, S., & Evans, R. J. (2016). A Markov decision process-based opportunistic spectral access. IEEE Wireless Communication Letters, 5(5), 544–547.CrossRefGoogle Scholar
  39. 39.
    Eltom, H., Kandeepan, S., Liang, Y. C., Moran, B., & Evans, R. J. (2016). HMM based cooperative spectrum occupancy prediction using hard fusion. In IEEE ICC, pp. 669–675.Google Scholar
  40. 40.
    Arunthavanathan, S., Kandeepan, S., & Evans, R. (Dec. 2013). Reinforcement learning based secondary user transmissions in cognitive radio networks. Globecom Workshops (IEEE GC Wkshps), pp. 374–379.Google Scholar
  41. 41.
    Bkassiny, M., Li, Y., & Jayaweera, S. (2012). A survey on machine-learning techniques in cognitive radios. IEEE Communications Surveys and Tutorials, 99, 1–24.Google Scholar
  42. 42.
    Kaelbling, L., Littman, M., & Moore, A. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237–285.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Electrical and Computer EngineeringRMIT UniversityMelbourneAustralia
  2. 2.CREATE-NetTrentoItaly
  3. 3.Huawei TechnologiesLannionFrance
  4. 4.Department of EngineeringMacquarie UniversitySydneyAustralia

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