Joint Deployment and Beamforming Design of Aerial STAR-RIS Aided Networks with Reinforcement Learning
This addresses performance enhancement in dynamic wireless networks for users, but it is incremental as it builds on existing STAR-RIS concepts with a new control method.
The paper tackles the problem of optimizing aerial STAR-RIS deployment and beamforming to dynamically control user grouping, achieving 57.1% and 285% sum-rate gains over fixed-deployment and RIS-free baselines.
Aerial simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) enables full-space coverage in dynamic wireless networks. However, most existing works assume fixed user grouping, overlooking the fact that STAR-RIS deployment inherently determines whether users are served via transmission or reflection. To address this, we propose a joint deployment and beamforming framework, where an aerial STAR-RIS dynamically adjusts its location and orientation to adaptively control user grouping and enhance hybrid beamforming. We formulate a Markov decision process (MDP) capturing the coupling among deployment, grouping, and signal design. To solve the resulting non-convex and time-varying problem, we develop a PPO-based reinforcement learning algorithm that adaptively balances user grouping and beamforming resources through online policy learning. Simulation results show 57.1\% and 285\% sum-rate gains over fixed-deployment and RIS-free baselines, respectively, demonstrating the benefit of user-grouping-aware control in STAR-RIS-aided systems.