Energy Efficiency Optimization in UAV-Assisted Communications and Edge Computing

Using unmanned aerial vehicles (UAVs) as aerial base stations has recently emerged as a promising solution to provide rapid connectivity in several scenarios. Motivated by these, we study a wireless network in which a UAV acts as a new aerial platform and serve terrestrial non-orthogonal multiple access (NOMA) users. In particular, we assume that the UAV acts as a mobile edge computing (MEC) node, offloading computation from the NOMA users. Our goal is to minimize the total power consumption in the network subject to the minimum achievable rate requirements according to the deadline constraints for the computation task of each user. We propose a framework to optimize both the power allocation and the trajectory of the UAV. To deal with the coupled parameters in the optimization, we decompose the optimization into three subproblems in order to optimize the power allocation, minimum amount of data to be processed per user per time slot, and trajectory of UAV respectively. Simulation results demonstrate that the NOMA approach outperforms orthogonal multiple access (OMA) in terms of energy consumption significantly.