Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks

TL;DR

This paper introduces a reinforcement learning-based anti-jamming strategy that deceives reactive jammers by manipulating channel selection, significantly improving communication robustness and efficiency in wireless networks.

Contribution

The paper proposes a novel RL-based anti-jamming approach that deceives jammers and maintains communication, outperforming existing RL and random strategies.

Findings

Achieves over 50% of maximum TRP in single-user scenarios.

Performance improves with more users and channels.

Outperforms existing RL and random strategies in simulations.

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.