Trajectory and Location Optimization of Unmanned Aerial Vehicles (UAVs) in Emerging Wireless Networks

Unmanned aerial vehicles (UAVs) have recently been successively utilized in many diverse areas, including wireless communications. Several applications have been mentioned for UAV-assisted communications including UAVs acting as base stations and data collection units for Internet of Things (IoT). Despite their effectiveness, there are still several fundamental challenges in UAV wireless systems that are yet to be resolved, mainly due to the practical constraints imposed by the UAV terminals. In this thesis, we aim to resolve some of these challenges to provide energy efficient, reliable, and high-rate deployment of UAVs. In particular, we study (i) optimal placement and trajectory design of UAVs to minimize the average user power consumption subject to a movement constraint on UAVs, (ii) optimal deployment of UAVs to minimize the user outage probability, and (iii) deep-learning assisted predictive deployment of UAVs for rate maximization. For all the studied scenarios, we consider practical wireless channel models specifically tailored for UAV communications. We also provide quantization-theory based analytical expressions and distributed numerical algorithms for optimal deployments and trajectory planning of UAVs.