A novel air-to-ground communication paradigm is conceived, where an unmanned aerial vehicle (UAV)-mounted base station (BS) equipped with multiple antennas sends information to multiple ground users (GUs) with the aid of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In contrast to the conventional RIS whose main function is to reflect incident signals, the STAR-RIS is capable of both transmitting and reflecting the impinging signals from either side of the surface, thereby leading to full-space 360 degree coverage. However, the transmissive and reflective capabilities of the STAR-RIS require more complex transmission/reflection coefficient design. Therefore, in this work, a sum-rate maximization problem is formulated for the joint optimization of the UAV’s trajectory, the active beamforming at the UAV, and the passive transmission/reflection beamforming at the STAR-RIS. This cutting-edge optimization problem is also subject to the UAV’s flight safety, to the maximum flight duration constraint, as well as to the GUs’ minimum data rate requirements. Given the unknown locations of obstacles prior to the UAV’s flight, we provide an online decision making framework employing reinforcement learning (RL) to simultaneously adjust both the UAV’s trajectory as well as the active and passive beamformer. To enhance the system’s robustness against the associated uncertainties caused by limited sampling of the environment, a novel “distributionally-robust” RL (DRRL) algorithm is proposed for offering an adequate worst-case performance guarantee. Our numerical results unveil that: 1) the STAR-RIS assisted UAV communications benefit from significant sum-rate gain over the conventional reflecting-only RIS; and 2) the proposed DRRL algorithm achieves both more stable and more robust performance than the state-of-the-art RL algorithms.

中文翻译：

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同时发射和反射可重构智能地面（STAR-RIS）辅助无人机通信


构思了一种新颖的空对地通信范例，其中配备多个天线的无人机（UAV）安装的基站（BS）借助同时发射和反射的可重构设备向多个地面用户（GU）发送信息智能表面（STAR-RIS）。与主要功能是反射入射信号的传统 RIS 相比，STAR-RIS 能够传输和反射来自表面两侧的撞击信号，从而实现全空间 360 度覆盖。然而，STAR-RIS的透射和反射能力需要更复杂的透射/反射系数设计。因此，在这项工作中，提出了一个和速率最大化问题，用于无人机轨迹、无人机主动波束成形以及 STAR-RIS 被动传输/反射波束成形的联合优化。这个前沿的优化问题还受到无人机飞行安全、最大飞行时间限制以及GU的最低数据速率要求的影响。考虑到无人机飞行前障碍物的位置未知，我们提供了一个在线决策框架，采用强化学习（RL）来同时调整无人机的轨迹以及主动和被动波束形成器。为了增强系统针对因环境采样有限而引起的相关不确定性的鲁棒性，提出了一种新颖的“分布式鲁棒”强化学习（DRRL）算法，以提供足够的最坏情况性能保证。 我们的数值结果表明：1）STAR-RIS 辅助无人机通信受益于传统仅反射 RIS 的显着总速率增益； 2）所提出的 DRRL 算法比最先进的 RL 算法实现了更稳定、更鲁棒的性能。