Computation-efficiency-oriented resource optimization in NOMA-assisted multi-UAV-enabled MEC networks

Unmanned aerial vehicles (UAVs) can serve as aerial mobile edge computing (MEC) servers to provide computing services to Internet of Things smart devices (ISDs) with insufficient computing capacity on the ground. However, how to provide energy-efficient computing services by designing appropriate resource optimization strategy is still a challenge issue in UAV-enabled MEC networks. To this end, this paper proposes a computation efficiency (CE)-oriented partial offloading framework for UAV-enabled MEC networks, where the ISDs' computation bits and energy consumption are taken into account simultaneously. Specifically, we firstly divide the ISDs into multiple clusters and determine the deployment of UAVs by k-means method. Meanwhile, ISDs occupying the same subchannel in the same cluster can offload data through non-orthogonal multiple access (NOMA) technology. Then, the problem of maximizing the CE of the system is formulated by optimizing subchannel allocation, transmit power and computation resources of ISDs. To solve it, we propose a staged optimization approach by using matching theory, Dinkelbach and sequential convex programming (SCP) methods. Numerical results demonstrate the proposed scheme can achieve higher CE compared with other baseline schemes.