# Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks

arXiv: https://arxiv.org/abs/2103.14056 · HTML: https://mlsanity.com/paper/2103.14056/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 25 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [A Collaborative Multi-agent Reinforcement Learning Anti-jamming Algorithm in Wireless Networks](https://mlsanity.com/paper/1809.04374/) (2018)
- ["Jam Me If You Can'': Defeating Jammer with Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications](https://mlsanity.com/paper/1904.03897/) (2019)
- [Jamming in the Internet of Things: A Game-Theoretic Perspective](https://mlsanity.com/paper/1607.06255/) (2016)
- [Drones in Distress: A Game-Theoretic Countermeasure for Protecting UAVs Against GPS Spoofing](https://mlsanity.com/paper/1904.11568/) (2019)
- [Collaborative Spectrum Sensing in Tactical Wireless Networks](https://mlsanity.com/paper/2003.06884/) (2020)
- [UAV-Aided Cellular Communications with Deep Reinforcement Learning Against Jamming](https://mlsanity.com/paper/1805.06628/) (2018)

## Built on by (most cited first, 5 of 5)

- [Continual Deep Reinforcement Learning to Prevent Catastrophic Forgetting in Jamming Mitigation](https://mlsanity.com/paper/2410.10521/) (2024)
- [Jamming Pattern Recognition over Multi-Channel Networks: A Deep Learning Approach](https://mlsanity.com/paper/2112.11222/) (2021)
- [Recurrent Neural Network-based Anti-jamming Framework for Defense Against Multiple Jamming Policies](https://mlsanity.com/paper/2208.09518/) (2022)
- [Fast Adaptive Anti-Jamming Channel Access via Deep Q Learning and Coarse-Grained Spectrum Prediction](https://mlsanity.com/paper/2502.04963/) (2025)
- [Coordinated Anti-Jamming Resilience in Swarm Networks via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2512.16813/) (2025)

## Abstract (arXiv)

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.