标题： 面向移动用户的节能公平STAR-RIS 显示英文标题

标题： Energy Efficient Fair STAR-RIS for Mobile Users

摘要： 在本工作中，我们提出一种方法，以提高同时传输和反射可重构智能表面（STAR-RIS）的能效和公平性，确保在保持可靠通信的同时降低功耗。 为了实现这一目标，我们引入了一个新的参数，称为子表面分配变量，该变量决定了分配给每个用户的STAR-RIS元素数量。 然后，我们通过同时优化STAR-RIS的相位偏移和子表面分配变量来制定一个新颖的优化问题。 我们利用深度强化学习（DRL）技术来解决这个优化问题。 DRL模型预测STAR-RIS的相位偏移，并高效地将STAR-RIS的元素分配给用户。 此外，我们在DRL模型中引入了一个惩罚项，以在不使用时促进STAR-RIS元素的智能停用，从而提高能效。 通过大量实验，我们表明所提出的方法可以以节能的方式在传输和反射空间中为所有用户实现相当高且几乎相等的数据速率。 显示英文摘要

摘要： 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.