<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Modern networking systems can benefit from Cognitive Radio (CR) because it mitigates spectrum scarcity. CR is prone to jamming attacks due to shared communication medium that results in a drop of spectrum usage. Existing solutions to jamming attacks are frequently based on Q-learning and deep Q-learning networks. Such solutions have a reputation for slow convergence and learning, particularly when states and action spaces are continuous. This paper introduces a unique reinforcement learning driven anti-jamming scheme that uses adversarial learning mechanism to counter hostile jammers. A mathematical model is employed in the formulation of jamming and anti-jamming strategies based on deep deterministic policy gradients to improve their policies against each other. An open-AI gym-oriented customized environment is used to evaluate proposed solution concerning power-factor and signal-to-noise-ratio. The simulation outcome shows that the proposed anti-jamming solution allows the transmitter to learn more about the jammer and devise the optimal countermeasures than conventional algorithms.</p>
</abstract>

Modern networking systems can benefit from Cognitive Radio (CR) because it mitigates spectrum scarcity. CR is prone to jamming attacks due to shared communication medium that results in a drop of spectrum usage. Existing solutions to jamming attacks are frequently based on Q-learning and deep Q-learning networks. Such solutions have a reputation for slow convergence and learning, particularly when states and action spaces are continuous. This paper introduces a unique reinforcement learning driven anti-jamming scheme that uses adversarial learning mechanism to counter hostile jammers. A mathematical model is employed in the formulation of jamming and anti-jamming strategies based on deep deterministic policy gradients to improve their policies against each other. An open-AI gym-oriented customized environment is used to evaluate proposed solution concerning power-factor and signal-to-noise-ratio. The simulation outcome shows that the proposed anti-jamming solution allows the transmitter to learn more about the jammer and devise the optimal countermeasures than conventional algorithms.

A Model-Free Cognitive Anti-Jamming Strategy Using Adversarial Learning Algorithm

Department of Computer Science and Engineering

PESIT-Bangalore South Campus-Bangalore and Affiliated to Visvesvaraya Technological University

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Modern networking systems can benefit from Cognitive Radio (CR) because it mitigates spectrum scarcity. CR is prone to jamming attacks due to shared...

A Model-Free Cognitive Anti-Jamming Strategy Using Adversarial Learning Algorithm

Data publikacji: 10 lis 2022

Zakres stron: 56 - 72

Otrzymano: 31 sie 2022

Przyjęty: 20 paź 2022

DOI: https://doi.org/10.2478/cait-2022-0039

Słowa kluczowe
Cognitive radio network, Reinforcement learning, Smart jammer, Intelligent anti-jamming, Adversarial learning

© 2022 Y. Sudha et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Model-Free Cognitive Anti-Jamming Strategy Using Adversarial Learning Algorithm

Data publikacji: 10 lis 2022

Zakres stron: 56 - 72

Otrzymano: 31 sie 2022

Przyjęty: 20 paź 2022

DOI: https://doi.org/10.2478/cait-2022-0039

Słowa kluczoweCognitive radio network, Reinforcement learning, Smart jammer, Intelligent anti-jamming, Adversarial learning

© 2022 Y. Sudha et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Cognitive radio network, Reinforcement learning, Smart jammer, Intelligent anti-jamming, Adversarial learning