Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) has been considered as a promising auxiliary device to enhance the performance of the wireless network, where users located at the different sides of the surfaces can be simultaneously served by the transmitting and reflecting signals. In this paper, the energy efficiency (EE) maximization problem for a non-orthogonal multiple access (NOMA) assisted STAR-RIS downlink network is investigated. Due to the fractional form of the EE, it is challenging to solve the EE maximization problem by the traditional convex optimization solutions. In this work, a deep deterministic policy gradient (DDPG)-based algorithm is proposed to maximize the EE by jointly optimizing the transmission beamforming vectors at the base station and the coefficients matrices at the STAR-RIS. Simulation results demonstrate that the proposed algorithm can effectively maximize the system EE considering the time-varying channels.

翻译：同时传输和反映可重新配置的智能表面(STAR-RIS)被认为是一个有希望的辅助装置,可以提高无线网络的性能,在无线网络中,位于表面不同侧面的用户可以通过发送和反映信号同时发挥作用。在本文件中,对非横向多重接入(NOMA)辅助STAR-RIS下行链路网络的能源效率最大化问题进行了调查。由于EE的分形形式,用传统的convex优化解决方案解决EEE最大化问题具有挑战性。在这项工作中,建议采用基于深度确定性政策梯度(DPG)的算法,通过联合优化基站的传输波束矢量和STAR-RIS的系数矩阵,最大限度地扩大EE。模拟结果表明,考虑到时间变化的渠道,拟议的算法可以有效地使EEE系统最大化。