Multiagent Deep Reinforcement Learning for AAV-RIS-Assisted Integrated Sensing and Communication

The integration of autonomous aerial vehicles (AAVs) and reconfigurable intelligent surfaces (RISs) in sixth-generation (6G) networks offers significant potential for enhancing integrated sensing and communication (ISAC) systems. Motivated by the need for real-time adaptability, spectrum efficiency, and intelligent wireless infrastructure, this article investigates a AAV-RIS-enabled ISAC framework capable of meeting the dual demands of sensing and communication in complex environments. A key challenge in such systems lies in dynamically balancing resource allocation, AAV trajectory planning, and interference management. To address this, we propose a joint optimization framework in which a AAV simultaneously conducts sensing operations and serves multiple ground users in obstructed environments. The formulated problem involves a nonconvex tradeoff between maximizing sensing signal-to-noise ratio (SNR) and communication throughput. To solve it, we develop a multiagent deep reinforcement learning (MADRL) solution. Three collaborative agents independently optimize AAV trajectories using deep deterministic policy gradient (DDPG), RIS phase shifts using twin delayed DDPG (TD3), and beamforming matrices using proximal policy optimization (PPO). These agents learn optimal policies under dynamic channel conditions and mobility constraints. Simulation results show that the proposed approach improves sensing SNR by 22% and communication rates by 20% compared to baseline methods. With equal weight factors for sensing and communication, the system achieves 95% of the maximum theoretical sensing SNR while ensuring a minimum communication rate of 1 bps/Hz, validating its effectiveness in real-time ISAC environments.