In this work, we propose a method to improve the energy efficiency and fairness of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) for mobile users, ensuring reduced power consumption while maintaining reliable communication. To achieve this, we introduce a new parameter known as the subsurface assignment variable, which determines the number of STAR-RIS elements allocated to each user. We then formulate a novel optimization problem by concurrently optimizing the phase shifts of the STAR-RIS and subsurface assignment variable. We leverage the deep reinforcement learning (DRL) technique to address this optimization problem. The DRL model predicts the phase shifts of the STAR-RIS and efficiently allocates elements of STAR-RIS to the users. Additionally, we incorporate a penalty term in the DRL model to facilitate intelligent deactivation of STAR-RIS elements when not in use to enhance energy efficiency. Through extensive experiments, we show that the proposed method can achieve fairly high and nearly equal data rates for all users in both the transmission and reflection spaces in an energy-efficient manner.

翻译：本文提出一种方法，旨在提升面向移动用户的同时透射反射可重构智能表面（STAR-RIS）的能量效率与公平性，在保证可靠通信的同时降低功耗。为实现这一目标，我们引入了一种称为子表面分配变量的新参数，该参数决定了分配给每个用户的STAR-RIS单元数量。随后，我们通过联合优化STAR-RIS的相移与子表面分配变量，构建了一个新颖的优化问题。我们利用深度强化学习（DRL）技术来解决该优化问题。该DRL模型预测STAR-RIS的相移，并高效地将STAR-RIS单元分配给用户。此外，我们在DRL模型中引入了一个惩罚项，以便在STAR-RIS单元闲置时智能地将其关闭，从而提升能量效率。通过大量实验，我们证明所提方法能够以能量高效的方式，为透射空间和反射空间中的所有用户实现相当高且近乎相等的数据速率。