DRL-based Energy Efficient Resource Allocation for STAR-RIS Assisted Coordinated Multi-cell Networks
Jian Chen, Ziye Ma, Yixuan Zou, Jie Jia, Xingwei Wang
Abstract
A novel simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) assisted collabo-rative multi-cell network is proposed. Cell-center and cell-edge users are enhanced by the STAR-RIS's reflection and transmission functions, respectively. We propose an online joint active and passive beamforming framework to maximize this system's long-term energy efficiency (EE) under time-varying channels and users' requirements. We first invoke fractional programming (FP) to optimize the coordinated zero-forcing beamforming among all base stations and to construct an interference-free transmission during each time slot. Then, a parallel deep reinforcement learning (DRL) algorithm is proposed to facilitate the online optimization of the passive beamforming of all STAR-RISs. Finally, the beamforming calculated by the FP algorithm is utilized as a part of the reward function of the DRL. As a result, the size of the action and state space is reduced, and the dynamic joint optimization is realized. Extensive numerical results reveal that: 1) the proposed algorithm induces a low computation complexity compared with conventional DRL algorithms, and 2) the STAR-RIS enhanced system can achieve higher EE than systems without RIS or with conventional reflection/transmission-only RISs.