Toward Proactive, Secure and Efficient Space-Air-Ground Communications: Generative AI-Based DRL Framework

The rapid growth of low-Earth-orbit (LEO) satellites has enabled integrated space-air-ground networks to provide seamless connectivity to mobile users. However, these networks face challenges such as physical layer security risks from line-of-sight channels and the energy constraints of high-altitude platforms (HAPs), necessitating solutions for secure communication and energy efficiency. In this work, we address the challenges of energy efficiency and secure communication in space-air-ground networks, which are becoming critical with the increasing deployment of LEO satellites to support high-mobility users. We propose a novel downlink architecture where high-altitude platforms (HAPs) assist the LEO satellite in serving ground users. To tackle the demands of secrecy energy efficiency (SEE) in this dynamic and complex network, we formulate a non-convex optimization problem that jointly considers HAP trajectory, user-HAP association, and beamforming. The problem’s non-convexity makes it computationally challenging to solve in polynomial time. To overcome these challenges, we introduce a generative artificial intelligence (GAI)-based deep reinforcement learning (DRL) framework, named Gen-DRL, which leverages generative adversarial networks to empower its agents. This framework dynamically predicts and adapts to changes in the space-air-ground network environment by optimizing key parameters such as channel states, HAP trajectories, user associations, and beamforming. Compared to conventional methods, the proposed Gen-DRL achieves significant improvements in SEE by effectively managing complex interdependencies among multiple agents and intelligently adapting to the network’s goals and constraints. Extensive simulation results demonstrate that Gen-DRL consistently outperforms existing state-of-the-art frameworks in terms of secrecy energy efficiency, robustness to dynamic user locations, and adaptability to varying network parameters. This work provides new insights into the design of secure and energy-efficient space-air-ground networks, highlighting the potential of GAI-based DRL for future communication systems.