标题： DRL-AdaPart：DRL驱动的自适应STAR-RIS划分以实现公平和节约的资源利用 显示英文标题

标题： DRL-AdaPart: DRL-Driven Adaptive STAR-RIS Partitioning for Fair and Frugal Resource Utilization

摘要： 在本工作中，我们提出了一种方法，用于高效利用同时发射和反射可重构智能表面（STAR-RIS）元件，以确保公平且高速的数据速率。我们引入了一个子表面分配变量，该变量决定了分配给每个用户的STAR-RIS元件数量，并通过使用适当调整的深度强化学习（DRL）算法联合优化STAR-RIS的相位偏移和子表面分配变量来最大化数据速率之和。所提出的DRL方法还与Dinkelbach算法和设计的混合DRL方法进行了比较。在DRL模型中引入了一个惩罚项，通过智能地在不需要时停用STAR-RIS元件来提高资源利用率。通过广泛的仿真，所提出的DRL方法可以在确保高效资源利用的同时，为静态和移动用户实现公平且高速的数据速率。使用所提出的DRL方法，在静态和移动场景中，最多可以分别停用27%和21%的STAR-RIS元件，而不会影响性能。 显示英文摘要

摘要： In this work, we propose a method for efficient resource utilization of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) elements to ensure fair and high data rates. We introduce a subsurface assignment variable that determines the number of STAR-RIS elements allocated to each user and maximizes the sum of the data rates by jointly optimizing the phase shifts of the STAR-RIS and the subsurface assignment variables using an appropriately tailored deep reinforcement learning (DRL) algorithm. The proposed DRL method is also compared with a Dinkelbach algorithm and the designed hybrid DRL approach. A penalty term is incorporated into the DRL model to enhance resource utilization by intelligently deactivating STAR-RIS elements when not required. The proposed DRL method can achieve fair and high data rates for static and mobile users while ensuring efficient resource utilization through extensive simulations. Using the proposed DRL method, up to 27% and 21% of STAR-RIS elements can be deactivated in static and mobile scenarios, respectively, without affecting performance.