Mounting a reconfigurable intelligent surface (RIS) on an unmanned aerial vehicle (UAV) holds promise for improving traditional terrestrial network performance. Unlike conventional methods deploying passive RIS on UAVs, this study delves into the efficacy of an aerial active RIS (AARIS). Specifically, the downlink transmission of an AARIS network is investigated, where the base station (BS) leverages rate-splitting multiple access (RSMA) for effective interference management and benefits from the support of an AARIS for jointly amplifying and reflecting the BS's transmit signals. Considering both the non-trivial energy consumption of the active RIS and the limited energy storage of the UAV, we propose an innovative element selection strategy for optimizing the on/off status of RIS elements, which adaptively and remarkably manages the system's power consumption. To this end, a resource management problem is formulated, aiming to maximize the system energy efficiency (EE) by jointly optimizing the transmit beamforming at the BS, the element activation, the phase shift and the amplification factor at the RIS, the RSMA common data rate at users, as well as the UAV's trajectory. Due to the dynamicity nature of UAV and user mobility, a deep reinforcement learning (DRL) algorithm is designed for resource allocation, utilizing meta-learning to adaptively handle fast time-varying system dynamics. Simulations indicate that incorporating an active RIS at the UAV leads to substantial EE gain, compared to passive RIS-aided UAV. We observe the superiority of the RSMA-based AARIS system in terms of EE, compared to existing approaches adopting non-orthogonal multiple access (NOMA).

翻译：在无人机上安装可重构智能表面有望改善传统地面网络性能。不同于在无人机上部署无源RIS的传统方法，本研究深入探讨了空中有源RIS（AARIS）的有效性。具体而言，研究了AARIS网络的下行传输，其中基站利用速率分割多址（RSMA）进行有效的干扰管理，并受益于AARIS的支持以联合放大和反射基站的发射信号。考虑到有源RIS的显著能耗和无人机的有限储能，我们提出了一种创新的元素选择策略来优化RIS元素的开关状态，从而自适应且显著地管理系统功耗。为此，构建了一个资源管理问题，旨在通过联合优化基站的发射波束赋形、RIS的元素激活、相移和放大因子、用户的RSMA公共数据速率以及无人机的轨迹来最大化系统能效。针对无人机和用户移动性的动态特性，设计了一种基于深度强化学习的资源分配算法，利用元学习自适应处理快速时变系统动态。仿真表明，与无源RIS辅助的无人机相比，在无人机上集成有源RIS可带来显著的能效提升。我们观察到，与采用非正交多址（NOMA）的现有方法相比，基于RSMA的AARIS系统在能效方面具有优越性。