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In the new generation of wireless communication systems, reconfigurableintelligent surfaces (RIS) and simultaneously transmitting and reflectingreconfigurable intelligent surfaces (STAR-RIS) have become competitive networkcomponents to achieve intelligent and reconfigurable network environments.However, existing work has not fully studied the deployment freedom ofSTAR-RIS, which limits further improvements in network communicationperformance. Therefore, this paper proposes a solution based on a deepreinforcement learning algorithm to dynamically deploy STAR-RIS and hybridbeamforming to improve the total communication rate of users in mobile wirelessnetworks. The paper constructs a STAR-RIS assisted multi-user multiple-inputsingle-output (MU-MISO) mobile wireless network and jointly optimizes thedynamic deployment strategy of STAR-RIS and the hybrid beamforming strategy tomaximize the long-term total communication rate of users. To solve thisproblem, the paper uses the Proximal Policy Optimization (PPO) algorithm tooptimize the deployment of STAR-RIS and the joint beamforming strategy ofSTAR-RIS and the base station. The trained policy can maximize the downlinktransmission rate of the system and meet the real-time decision-making needs ofthe system. Numerical simulation results show that compared with thetraditional scheme without using STAR-RIS and fixed STAR-RIS deployment, thePPO method proposed in this paper can effectively improve the totalcommunication rate of wireless network users in the service area.

Deep Reinforcement Learning Enabled Joint Deployment and Beamforming in STAR-RIS Assisted Networks