Joint Optimization of STARS-Assisted Airn–Ground ISAC System Using Deep Reinforcement Learning

Unmanned Aerial Vehicles (UAVs) are highly promising platforms with significant potential benefits for integrated sensing and communication (ISAC). However, optimizing the UAV trajectory within the ISAC system is challenging for traditional optimization methods due to numerous coupled variables. Additionally, dynamic user access and disconnection demand scalability beyond conventional methods. To address these challenges, this paper proposes the attention-based decoupling space (AT-DS) framework. It employs one agent with an attention-based actor (A2) module for STARS beamforming and power allocation and another agent with an original actor network for UAV flight control. Our simulation results indicate that: 1) AT-DS, compared to other baseline methods, can simultaneously maximize ISAC performance and accomplish the task of reaching the destination within limited energy, achieving optimal overall performance; and 2) AT-DS exhibits scalability, adapting to variations in the number of communication users in the system during ISAC services.