文章基本信息

标题：Deep learning–based fifth-generation millimeter-wave communication channel tracking for unmanned aerial vehicle Internet of things networks
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作者：Shan Meng ; Xin Dai ; Bicheng Xiao 等
期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2019
卷号：15
期号：8
页码：1
DOI：10.1177/1550147719865882
出版社：Hindawi Publishing Corporation
摘要：Using unmanned aerial vehicle as movable base stations is a promising approach to enhance network coverage. Moreover, movable unmanned aerial vehicle–base stations can dynamically move to the target devices to expand the communication range as relays in the scenario of the Internet of things. In this article, we consider a communication system with movable unmanned aerial vehicle–base stations in millimeter-Wave. The movable unmanned aerial vehicle–base stations are equipped with antennas and multiple sensors for channel tracking. The cylindrical array antenna is mounted on the movable unmanned aerial vehicle–movable base stations, making the beam omnidirectional. Furthermore, the attitude estimation method using the deep neural network can replace the traditional attitude estimation method. The estimated unmanned aerial vehicle attitude information is combined with beamforming technology to realize a reliable communication link. Simulation experiments have been performed, and the results have verified the effectiveness of the proposed method.
关键词：movable unmanned aerial vehicle–base stations; attitude estimation; deep neural network; beamforming; Internet of things
其他关键词：movable unmanned aerial vehicle–base stations ; attitude estimation ; deep neural network ; beamforming ; Internet of things