Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks

Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks

A. Pourranjbar, G. Kaddoum, A. Ferdowsi and W. Saad, “Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks,” in IEEE Transactions on Communications, vol. 69, no. 6, pp. 3682-3697, June 2021, doi: 10.1109/TCOMM.2021.3062854.

Abstract or Summary

Conventional anti-jamming methods mostly rely on frequency hopping to hide or escape from jammers. These approaches are not efficient in terms of bandwidth usage and can also result in a high probability of jamming. Different from existing works, in this article, a novel anti-jamming strategy is proposed based on the idea of deceiving the jammer into attacking a victim channel while maintaining the communications of legitimate users in safe channels. Since the jammer’s channel information is not known to the users, an optimal channel selection scheme and a sub-optimal power allocation algorithm are proposed using reinforcement learning (RL). The performance of the proposed anti-jamming technique is evaluated by deriving the statistical lower bound of the total received power (TRP). Analytical results show that, for a given access point, over 50% of the highest achievable TRP, i.e. in the absence of jammers, is achieved for the case of a single user and three frequency channels. Moreover, this value increases with the number of users and available channels. The obtained results are compared with two existing RL based anti-jamming techniques, and a random channel allocation strategy without any jamming attacks. Simulation results show that the proposed anti-jamming method outperforms the compared RL based anti-jamming methods and the random search method, and yields near optimal achievable TRP.

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