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

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.