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.

本研究提出了一种方法来提高移动用户进行同时传输和反射的可重构智能表面（STAR-RIS）的能量效率和公平性，以确保降低功耗同时保持可靠的通信。通过引入一种新的参数，即次表面分配变量，来确定分配给每个用户的STAR-RIS元素的数量。我们同时优化STAR-RIS的相位偏移和次表面分配变量，将这一优化问题转化为一种新颖的优化问题。我们利用深度强化学习（DRL）技术来解决这个优化问题。DRL模型预测STAR-RIS的相位偏移，并高效地分配STAR-RIS的元素给用户。此外，我们在DRL模型中加入了一个惩罚项，以促进非使用状态下的STAR-RIS元素的智能停用，以增强能量效率。通过广泛的实验，我们证明了所提出的方法可以在能量高效的条件下，为传输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。

移动用户的高效公平STAR-RIS