标题： 城市低空毫米波通信中无人机载IRS的联合优化深度强化学习 显示英文标题

标题： Joint Optimization of UAV-Carried IRS for Urban Low Altitude mmWave Communications with Deep Reinforcement Learning

摘要： 第六代（6G）无线通信的新兴技术，如太赫兹通信和超大规模多输入多输出，展现出良好的前景。 尽管毫米波通信具有高数据速率的潜力，但在城市低空经济（LAE）环境中的毫米波（mmWave）通信受到信号衰减和多径干扰等挑战的限制。 特别是，在城市环境中，由于毫米波的短波长，建筑物会导致显著的信号衰减，这需要开发创新的方法来提高LAE网络中此类通信的鲁棒性。 在本文中，我们探讨了使用无人机（UAV）携带智能反射表面（IRS）来支持低空毫米波通信。 具体而言，我们考虑了一个典型的城市低空通信场景，其中无人机携带的IRS在存在障碍物的情况下，在移动用户和源用户（SU）之间建立视距（LoS）信道。 随后，我们制定一个优化问题，通过联合优化IRS的相移和无人机轨迹来最大化传输速率并最小化无人机的能量消耗。 鉴于问题的非凸性和高动态性，我们提出了一种基于深度强化学习的方法，结合神经情景控制、长短时记忆和IRS相移控制方法，以提高稳定性和加速收敛。 仿真结果表明，所提出的算法有效解决了问题，并在各种性能上优于其他基准算法。 显示英文摘要

摘要： Emerging technologies in sixth generation (6G) of wireless communications, such as terahertz communication and ultra-massive multiple-input multiple-output, present promising prospects. Despite the high data rate potential of millimeter wave communications, millimeter wave (mmWave) communications in urban low altitude economy (LAE) environments are constrained by challenges such as signal attenuation and multipath interference. Specially, in urban environments, mmWave communication experiences significant attenuation due to buildings, owing to its short wavelength, which necessitates developing innovative approaches to improve the robustness of such communications in LAE networking. In this paper, we explore the use of an unmanned aerial vehicle (UAV)-carried intelligent reflecting surface (IRS) to support low altitude mmWave communication. Specifically, we consider a typical urban low altitude communication scenario where a UAV-carried IRS establishes a line-of-sight (LoS) channel between the mobile users and a source user (SU) despite the presence of obstacles. Subsequently, we formulate an optimization problem aimed at maximizing the transmission rates and minimizing the energy consumption of the UAV by jointly optimizing phase shifts of the IRS and UAV trajectory. Given the non-convex nature of the problem and its high dynamics, we propose a deep reinforcement learning-based approach incorporating neural episodic control, long short-term memory, and an IRS phase shift control method to enhance the stability and accelerate the convergence. Simulation results show that the proposed algorithm effectively resolves the problem and surpasses other benchmark algorithms in various performances.