Computer Vision Aided mmWave UAV Communication Systems

• Published in: IEEE internet of things journal 2023-07, Vol.10 (14), p.1-1
• Main Authors: Hua, Zizheng, Lu, Yang, Pan, Gaofeng, Gao, Kun, da Costa, Daniel Benevides, Chen, Su
• Format: Article
• Language: English
• Subjects: Autonomous aerial vehicles; Communication systems; Communications systems; Computer vision; deep learning; energy efficiency; Line of sight communication; Localization; Location awareness; Millimeter wave communication; Millimeter waves; Object detection; Optimization; Simultaneous localization and mapping; Topology; Trajectories; trajectory optimization; unmanned aerial vehicle communication; Unmanned aerial vehicles; Wireless communication
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2023.3251377

Unmanned aerial vehicle (UAV) communication systems usually operate in harsh scenarios, which require accurate information about the topology and wireless channel to achieve the desired transmission performance. Therefore, when millimeter wave (mmWave) communication with its intrinsic line-of-sight (LoS) condition is adopted, accurate target localization is essential to determine the spatial relationship between the UAV and the grounded receivers (Rxs). In this paper, a computer-vision (CV)-aided jointly optimization scheme of flight trajectory and power allocation is designed for mmWave UAV communication systems by utilizing the visual information captured via cameras equipped at the UAV. Compared with traditional schemes, the implementation cost and overhead can be greatly saved as no radio frequency transmissions are required in the proposed localization scheme. In addition, the transmit power at the UAV is jointly optimized with its flight trajectory in two different cases. Finally, simulation results are presented to demonstrate the efficiency of the proposed schemes.