论文标题
通过空中重新配置智能表面优化信息时代：一种深厚的增强学习方法
Optimizing Age of Information Through Aerial Reconfigurable Intelligent Surfaces: A Deep Reinforcement Learning Approach
论文作者
论文摘要
我们研究了将无人机（UAV）与可重构智能表面（RIS）元素集成的好处，以被物联网设备（IOTD）采样的被动中继信息到基础站（BS）。为了保持传递信息的新鲜感，配制了一个优化问题，以最大程度地降低预期总和信息（AOI）的目的，以优化无人机的高度，通信时间表和RIS元素的阶段。在缺乏IOTD的激活模式的先验知识的情况下，开发了近端策略优化算法来解决这一混合成分的非convex优化问题。数值结果表明，我们提出的算法在AOI方面优于所有其他算法。
We investigate the benefits of integrating unmanned aerial vehicles (UAVs) with reconfigurable intelligent surface (RIS) elements to passively relay information sampled by Internet of Things devices (IoTDs) to the base station (BS). In order to maintain the freshness of relayed information, an optimization problem with the objective of minimizing the expected sum Age-of-Information (AoI) is formulated to optimize the altitude of the UAV, the communication schedule, and phases-shift of RIS elements. In the absence of prior knowledge of the activation pattern of the IoTDs, proximal policy optimization algorithm is developed to solve this mixed-integer non-convex optimization problem. Numerical results show that our proposed algorithm outperforms all others in terms of AoI.
