Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks

• URL: http://arxiv.org/abs/2103.14056v1
• Date: Thu, 25 Mar 2021 18:12:41 GMT
• Title: Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks
• Authors: Ali Pourranjbar, Georges Kaddoum, Aidin Ferdowsi, and Walid Saad
• Abstract summary: A novel anti-jamming strategy is proposed based on the idea of deceiving the jammer into attacking a victim channel. Since the jammer's channel information is not known to the users, an optimal channel selection scheme and a sub optimal power allocation are proposed. Simulation results show that the proposed anti-jamming method outperforms the compared RL based anti-jamming methods and random search method.
• Score: 76.82565500647323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional anti-jamming method mostly rely on frequency hopping to hide or escape from jammer. 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 paper, 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 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 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 random search method, and yields near optimal achievable TRP.

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