Deep Reinforcement Learning-Enabled Trajectory and Bandwidth Allocation Optimization for UAV-Assisted Integrated Sensing and Covert Communication

The growing interest in integrated sensing and communication (ISAC) has accelerated the development of unmanned aerial vehicles (UAVs) and drones for secure data transmission. In this study, the optimization of UAV trajectory and bandwidth allocation within the ISAC framework is investigated, with a focus on covert communication under energy constraints. We propose a novel deep reinforcement learning (DRL) algorithm, Soft Actor-Critic for Covert Communication and Charging (SAC-CC), to address this problem. The SAC-CC algorithm maximizes the CCTR by dynamically allocating bandwidth for sensing and communication tasks while adjusting the UAV’s trajectory to manage energy consumption. This approach ensures accurate tracking of the adversarial UAV to maintain effective covert communication. Experimental results show that SAC-CC significantly outperforms existing DRL algorithms in CCTR and improves UAV endurance. Also, its robustness under different adversarial trajectories, covert communication requirements, and charging conditions is validated. Furthermore, the UAV’s flight altitude, along with the number and distribution pattern of adversarial UAVs, directly affect covert communication performance. Finally, the study emphasizes the trade-offs among bandwidth allocation, sensing accuracy, and the balance between power spectral density and UAV energy capacity, providing key insights for the practical configuration of bandwidth and energy parameters in UAV-assisted ISAC systems.