Reflections in the Sky: Millimeter Wave Communication with UAV-Carried Intelligent Reflectors
Reflections in the Sky: Millimeter Wave Communication with UAV-Carried Intelligent Reflectors
复制标题
DOI：
10.1109/globecom38437.2019.9013626
复制
发表时间：
2019-08
期刊：
影响因子：
--
通讯作者：
Qianqian Zhang;W. Saad;M. Bennis
中科院分区：
文献类型：
--
作者：
Qianqian Zhang;W. Saad;M. Bennis
提出了一种利用无人机携带的智能反射器(IR)来提高毫米波网络性能的新方法。特别是，无人机-IR被用来智能地将来自基站的毫米波波束形成信号反射到移动的户外用户，同时从毫米波信号中收集能量来为红外供电。为了维持视距(LOS)信道，提出了一种基于Q学习和神经网络的强化学习(RL)方法来对传播环境进行建模，从而优化无人机-IR的位置和反射系数以最大化下行链路的传输容量。仿真结果表明，在平均数据速率和可实现的下行链路LOS概率方面，使用无人机-IR比使用静态IR具有显著的优势。结果还表明，与无学习的方案相比，基于RL的无人机-IR部署进一步提高了网络性能。
In this paper, a novel approach that uses an unmanned aerial vehicle (UAV)-carried intelligent reflector (IR) is proposed to enhance the performance of millimeter wave (mmW) networks. In particular, the UAV-IR is used to intelligently reflect mmW beamforming signals from a base station towards a mobile outdoor user, while harvesting energy from mmW signals to power the IR. To maintain a line-of-sight (LOS) channel, a reinforcement learning (RL) approach, based on Q- learning and neural networks, is proposed to model the propagation environment, such that the location and reflection coefficient of the UAV-IR can be optimized to maximize the downlink transmission capacity. Simulation results show a significant advantage for using a UAV-IR over a static IR, in terms of the average data rate and the achievable downlink LOS probability. The results also show that the RL-based deployment of the UAV-IR further improves the network performance, relative to a scheme without learning.