Analysis Of Deep Learning-Based Anti-Jamming Method for UAV Communication

Abstract

With the increasing popularity of Unmanned Aerial Vehicle(UAV) in both military and civilian applications , it is crucial to ensure the reliability and safety of their signals. In recent years, deep reinforcement learning has proven to be a powerful tool for meeting this challenge. This paper aims to analyze the potential of deep learning in signal jamming countermeasures for UAV communication systems. Traditional jamming countermeasures, such as frequency hopping and Direct Sequence Spread Spectrum (DSSS), are effective to a certain extent. Still, it is difficult to cope with dynamically changing jamming environments due to single mode and low spectrum utilization. On the other hand, deep learning has powerful automatic feature extraction and pattern recognition capabilities, especially the reinforcement learning method based on deep learning, which can realize real-time adaptation of UAVs to environmental changes and automatic optimization of communication strategies. The article focuses on anti-jamming methods based on reinforcement learning and deep reinforcement learning algorithms and proposes a new anti-jamming strategy for UAV communication based on generative adversarial networks (GANs). Although the deep learning method opens up a new way for anti-jamming of UAV communication signals, it still faces the challenges of high demand for computational resources and complex model training in practical applications.