论文标题
智能反射表面辅助无人机通信的联合轨迹和被动横梁形成设计：一种深厚的增强学习方法
Joint Trajectory and Passive Beamforming Design for Intelligent Reflecting Surface-Aided UAV Communications: A Deep Reinforcement Learning Approach
论文作者
论文摘要
在本文中，研究了智能反射表面（IRS）辅助的无人驾驶飞机（UAV）通信系统，在其中部署了无人机为用户设备（UE）提供服务，并借助安装在几座建筑物上的多个IRS的帮助，以增强无人机和UE之间的通信质量。我们旨在通过共同优化UAV的轨迹以及反射IRS的相变，包括UE的数据速率和无人机的能源消耗，包括UE的相位转移，当UE移动和IRS的选择被认为是为了节省能源时。由于系统很复杂并且环境是动态的，因此使用常规优化方法来得出低复杂性算法是一项挑战。为了解决这个问题，我们首先提出了一种基于Q-Network（DQN）的算法，通过离散轨迹，这具有训练时间的优势。此外，我们提出了基于深层的确定性政策梯度（DDPG）算法，以通过连续轨迹来解决该案例，以实现更好的性能。实验结果表明，与其他传统解决方案相比，所提出的算法具有相当大的性能。
In this paper, the intelligent reflecting surface (IRS)-aided unmanned aerial vehicle (UAV) communication system is studied, where the UAV is deployed to serve the user equipment (UE) with the assistance of multiple IRSs mounted on several buildings to enhance the communication quality between UAV and UE. We aim to maximize the energy efficiency of the system, including the data rate of UE and the energy consumption of UAV via jointly optimizing the UAV's trajectory and the phase shifts of reflecting elements of IRS, when the UE moves and the selection of IRSs is considered for the energy saving purpose. Since the system is complex and the environment is dynamic, it is challenging to derive low-complexity algorithms by using conventional optimization methods. To address this issue, we first propose a deep Q-network (DQN)-based algorithm by discretizing the trajectory, which has the advantage of training time. Furthermore, we propose a deep deterministic policy gradient (DDPG)-based algorithm to tackle the case with continuous trajectory for achieving better performance. The experimental results show that the proposed algorithms achieve considerable performance compared to other traditional solutions.
