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

arXiv: https://arxiv.org/abs/2208.09518 · HTML: https://mlsanity.com/paper/2208.09518/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 19 Aug 2022.

## Builds on (most cited first, 8 of 8)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [A Collaborative Multi-agent Reinforcement Learning Anti-jamming Algorithm in Wireless Networks](https://mlsanity.com/paper/1809.04374/) (2018)
- [Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks](https://mlsanity.com/paper/2103.14056/) (2021)
- [Defeating jamming with the power of silence: a game-theoretic analysis](https://mlsanity.com/paper/1412.6687/) (2014)
- [Collaborative Spectrum Sensing in Tactical Wireless Networks](https://mlsanity.com/paper/2003.06884/) (2020)
- [Energy-Efficient Trajectory Design for UAV-Enabled Communication Under Malicious Jamming](https://mlsanity.com/paper/2009.06161/) (2020)
- [Jamming Pattern Recognition over Multi-Channel Networks: A Deep Learning Approach](https://mlsanity.com/paper/2112.11222/) (2021)

## Abstract (arXiv)

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.