Recurrent Neural Network-based Anti-jamming Framework for Defense Against Multiple Jamming Policies

TL;DR

This paper introduces an RNN-based anti-jamming framework capable of adapting to various jamming policies and estimating future occupied channels, significantly improving transmission success rates over traditional Q-learning methods.

Contribution

It proposes a novel RNN-based anti-jamming approach that adapts to multiple jamming policies and predicts future channel occupancy, enhancing robustness against diverse attack strategies.

Findings

Achieves over 75% successful transmission rate under 70% spectrum jamming.

Outperforms baseline Q-learning method across all tested scenarios.

Maintains high ergodic rates even with multiple jammers present.

Abstract

Conventional anti-jamming methods mainly focus on preventing single jammer attacks with an invariant jamming policy or jamming attacks from multiple jammers with similar jamming policies. These anti-jamming methods are ineffective against a single jammer following several different jamming policies or multiple jammers with distinct policies. Therefore, this paper proposes an anti-jamming method that can adapt its policy to the current jamming attack. Moreover, for the multiple jammers scenario, an anti-jamming method that estimates the future occupied channels using the jammers' occupied channels in previous time slots is proposed. In both single and multiple jammers scenarios, the interaction between the users and jammers is modeled using recurrent neural networks (RNN)s. The performance of the proposed anti-jamming methods is evaluated by calculating the users' successful transmission rate (STR) and ergodic rate (ER), and compared to a baseline based on Q-learning (DQL). Simulation results show that for the single jammer scenario, all the considered jamming policies are perfectly detected and high STR and ER are maintained. Moreover, when 70 % of the spectrum is under jamming attacks from multiple jammers, the proposed method achieves an STR and ER greater than 75 % and 80 %, respectively. These values rise to 90 % when 30 % of the spectrum is under jamming attacks. In addition, the proposed anti-jamming methods significantly outperform the DQL method for all the considered cases and jamming scenarios.