Energy-Efficient Strategic AAV-Enabled MEC Networks via STAR-RIS: Joint Optimization of Trajectory and User Association

The deployment of Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS) has proven to be an effective means to extend coverage and improve wireless signal quality. STAR-RIS in wireless networks for aided autonomous aerial vehicle (AAV) communications enables a significant boost in network capacity and the provision of virtual line-of-sight (LoS) links to efficiently meet the quality-of-service (QoS) requirements of user equipment (UE). Accordingly, this article proposes a novel STAR-RIS-aided multi-AAV communication framework to exploit energy efficiency and total throughput maximally. We formulate the long-term optimization problem as a decentralized, partially observed Markov decision process (DEC-POMDP). Then, we formulate the discrete association scheduling problem as a noncooperative theoretical game and propose the UA-CFG algorithm to realize the UE association scheme that converges to a Nash equilibrium (NE). Then, a multiagent reinforcement learning (MARL) method with well-established robustness is devised to continuously optimize the trajectories and energetic consumption of AAVs through centralized training and distributed implementation. Experimental results reveal that the performance of the proposed algorithm is considerable compared to other traditional schemes.