Quasi-Optimization of Resource Allocation and Positioning for Solar-Powered UAVs

Unmanned Aerial Vehicles (UAVs) will be an integral part of future smart cities to provide applications such as traffic management, environment monitoring, and data collection. UAVs offer flexible deployment, dynamic mobility, and Ultra-Reliable and Low Latency Communications (URLLC). However, UAVs are power-hungry devices, and their limited battery capacity cannot support their flight and communication operations for a long time. Additionally, multi-carrier (MC) techniques will be vital for supporting futuristic multi-user communication systems. To overcome these issues, we propose a solar-powered UAV MC system to support URLLC services for multi-users. In this regard, we aim to maximize the system sum throughput and we jointly optimize UAV positioning and sub-carrier allocation. To solve the optimization problem, we propose the low-complexity coordinate descent approximation algorithm (CDAA). Lastly, we show the proposed algorithm converges quickly and simultaneously yields superior performance compared to fixed benchmark schemes for two simulated environments.