Reinforcement Learning for Reactive Jamming Mitigation
DOI:
https://doi.org/10.13052/jcsm2245-1439.325Keywords:
Reactive jamming, reinforcement learning, Markov decision process, repeater jamming, Q-learningAbstract
In this paper, we propose a strategy to avoid or mitigate reactive forms of jamming using a reinforcement learning approach. The mitigation strategy focuses on finding an effective channel hopping and idling pattern to maximize link throughput. Thus, the strategy is well-suited for frequency-hopping spread spectrum systems, and best performs in tandem with a channel selection algorithm. By using a learning approach, there is no need to pre-program a radio with specific anti-jam strategies and the problem of having to classify jammers is avoided. Instead the specific anti-jam strategy is learned in real time and in the presence of the jammer.
Downloads
References
David L Adamy. EW 101. Artech House, 2001.
Joseph Aubrey Boyd, Donald B Harris, Donald D King, and HW Welch Jr. Electronic countermeasures. Electronic Countermeasures, 1, 1978.
S.M. Dudley, W.C. Headley, M. Lichtman, E.Y. Imana, Xiaofu Ma, M. Abdelbar, A. Padaki, A. Ullah, M.M. Sohul, Taeyoung Yang, and J.H. Reed. Practical issues for spectrum management with cognitive radios. Proceedings of the IEEE, 102(3): 242–264, March 2014.
Shabnam Sodagari and T Charles Clancy. An anti-jamming strategy for channel access in cognitive radio networks. In Decision and Game Theory for Security, pages 34–43. Springer, 2011.
Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction, volume 1. Cambridge Univ Press, 1998.
Don J Torrieri. Fundamental limitations on repeater jamming of frequency-hopping communications. Selected Areas in Communications, IEEE Journal on, 7(4): 569–575,
Yongle Wu, Beibei Wang, and KJ Ray Liu. Optimal defense against jamming attacks in cognitive radio networks using the markov decision process approach. In IEEE Global Telecommunications Conference 2010, pages 1–5. IEEE, 2010.
Wenyuan Xu, Wade Trappe, Yanyong Zhang, and Timothy Wood. The feasibility of launching and detecting jamming attacks in wireless networks. In Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing, pages 46–57. ACM, 2005.
Mengfei Yang and David Grace. Cognitive radio with reinforcement learning applied to heterogeneous multicast terrestrial communication systems. In Cognitive Radio Oriented Wireless Networks and Communications, 2009, pages 1–6. IEEE, 2009.
Yanmin Zhu, Xiangpeng Li, and Bo Li. Optimal adaptive antijamming in wireless sensor networks. International Journal of Distributed Sensor Networks, 2012, 2012.
