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

Published in:
2013 IEEE Conf. on Communications and Network Security (CNS), 14-16 October 2013, pp. 28-36.

Summary

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-for Q-learning can provide the best solution under distributed mobile ad hoc networking scenarios in which the centralized control can hardly be available.
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Summary

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...

READ MORE