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Moreover, movable unmanned aerial vehicle\u2013base 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\u2013base stations in millimeter-Wave. The movable unmanned aerial vehicle\u2013base stations are equipped with antennas and multiple sensors for channel tracking. The cylindrical array antenna is mounted on the movable unmanned aerial vehicle\u2013movable 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. <\/jats:p>","DOI":"10.1177\/1550147719865882","type":"journal-article","created":{"date-parts":[[2019,8,2]],"date-time":"2019-08-02T08:14:09Z","timestamp":1564733649000},"page":"155014771986588","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["Deep learning\u2013based fifth-generation millimeter-wave communication channel tracking for unmanned aerial vehicle Internet of things networks"],"prefix":"10.1177","volume":"15","author":[{"given":"Shan","family":"Meng","sequence":"first","affiliation":[{"name":"Guangdong Key Laboratory of Intelligent Information Processing, School of Electronics and Information Engineering, Shenzhen University, Shenzhen, 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