Joint Deployment and Beamforming Design of Aerial STAR-RIS Aided Networks with Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based framework for dynamically deploying and beamforming aerial STAR-RIS to optimize user grouping and improve wireless network performance.

Contribution

It presents a novel joint deployment and beamforming approach using reinforcement learning to adaptively control user grouping in aerial STAR-RIS systems.

Findings

Achieved 57.1% sum-rate improvement over fixed deployment.

Achieved 285% sum-rate improvement over RIS-free systems.

Demonstrated effectiveness of user-grouping-aware control in simulations.

Abstract

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.