Competing Mobile Network Game: Embracing antijamming and jamming strategies with reinforcement learning

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Competing Mobile Network Game: Embracing antijamming and jamming strategies with reinforcement learning

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Title: Competing Mobile Network Game: Embracing antijamming and jamming strategies with reinforcement learning
Author: Gwon, Youngjune Lee; Dastangoo, Siamak; Fossa, Carl; Kung, H. T.

Note: Order does not necessarily reflect citation order of authors.

Citation: Gwon, Youngjune, Siamak Dastangoo, Carl Fossa, and H. T. Kung. 2013. “Competing Mobile Network Game: Embracing Antijamming and Jamming Strategies with Reinforcement Learning.” In the Proceedings of the 2013 IEEE Conference on Communications and Network Security (CNS), National Harbor, MD and Washington DC, 14-16 October, 2013, 28-36. IEE Press.
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Abstract: We introduce Competing Mobile Network Game (CMNG), a stochastic game played by cognitive radio networks that compete for dominating an open spectrum access. Differentiated from existing approaches, we incorporate both communicator and jamming nodes to form a network for friendly coalition, integrate antijamming and jamming subgames into a stochastic framework, and apply Q-learning techniques to solve for an optimal channel access strategy. We empirically evaluate our Q-learning based strategies and find that Minimax-Q learning is more suitable for an aggressive environment than Nash-Q while Friend-or-foe Q-learning can provide the best solution under distributed mobile ad hoc networking scenarios in which the centralized control can hardly be available.
Published Version: doi:10.1109/CNS.2013.6682689
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:12561370
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