Deep learning–based fifth-generation millimeter-wave communication channel tracking for unmanned aerial vehicle Internet of things networks

Shan Meng, Xin Dai, Bicheng Xiao, Yimin Zhou, Yumei Li and Chong Gao

International Journal of Distributed Sensor Networks, 2019, vol. 15, issue 8, 1550147719865882

Abstract: 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.

Keywords: movable unmanned aerial vehicle–base stations; attitude estimation; deep neural network; beamforming; Internet of things (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:15:y:2019:i:8:p:1550147719865882

DOI: 10.1177/1550147719865882

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