A Review on Recent Approaches in mmWave UAV-aided Communication Networks and Open Issues

Recently, the use of unmanned aerial vehicles (UAVs) is spreading to many fields, especially for wireless communication-related tasks. However, such communication is facing many challenges, as the sub-6 GHz frequency band is now heavily occupied. As a result, millimeter-wave (mmWave) frequency band communication is now a promising technology to tackle that issue. By equipping multiple antennas to the UAV to perform 3D beamforming, as well as making good use of the flexible mobility of the UAV, we can greatly improve the communication link in mmWave communication systems. On the other hand, the trend of using intelligent-based learning methods, specifically reinforcement learning greatly increases recently, due to their ability to capture the dynamic of complex systems. In this study, we review recent approaches in mmWave UAV-aided communication networks. We first introduce the main characteristics of mmWave UAV networks, then we provide some insight into the recent trend of applying intelligent learning-based methods for solving this type of system. After that, some open issues and potential research directions for the mmWave UAV communication systems are provided.