Intelligent energy-efficient and fair resource scheduling for UAV-assisted space-air-ground integrated networks under jamming attacks

The space-air-ground integrated network (SAGIN) is a crucial technology for sixth-generation (6G) wireless communication networks to achieve seamless coverage and high throughput. In this paper, we propose an unmanned aerial vehicle (UAV)-assisted SAGIN structure, where the UAV is responsible for collecting data from ground users (GUs) and transmitting it to low-earth orbit (LEO) satellites. This paper also formulates a joint energy-efficient and fair resource scheduling optimization problem under jamming attacks and limited energy constraints, where the line-of-sight (LoS) links between the UAV and GUs are susceptible to being jammed. Due to the non-convex problem and dynamic environments, a deep reinforcement learning (DRL)-based twin delayed deep deterministic policy gradient (TD3) is developed to search optimal UAV trajectory to maximize energy efficiency (EE) and fairness against jamming. Simulation results verify that the proposed intelligent resource scheduling algorithm outperforms the baseline algorithms in terms of EE and fairness index in different settings.