Intelligent anti-jamming scheme of UAV swarm based on DQN

Unmanned Aerial Vehicle (UAV) ad-hoc networks, characterized by distributed and dynamic topologies, are susceptible to interference attacks. This study delves into strategies to enhance the reliability and performance of UAV networks, with a specific focus on anti-interference measures at the Medium Access Control (MAC) layer. Two primary interference channel selection methods, model-based and model-free, are discussed. Model-based methods rely on prior information to estimate system models and predict channel interference states. However, these methods necessitate an in-depth understanding of the interference environment, limiting adaptability. In contrast, model-free methods utilize reinforcement learning, interacting with the environment to acquire real-time channel information, offering superior adaptability. This paper introduces an intelligent anti-interference solution at the central node of UAV networks based on the Deep Q-Network (DQN) model. DQN, a reinforcement learning algorithm, is employed to learn optimal channel selection strategies, thereby enhancing overall anti-interference performance. Research findings indicate that the proposed intelligent anti-interference solution achieves a high level of accuracy in channel selection, demonstrating robust anti-interference performance.