UAV-Enabled Mobile-Edge Computing for AI Applications: Joint Model Decision, Resource Allocation, and Trajectory Optimization

Due to the flexible mobility and agility, unmanned aerial vehicles (UAVs) are expected to be deployed as aerial base stations (BSs) in future air–ground-integrated wireless networks, providing temporary and controllable coverage and additional computation capabilities for ground Internet of Things (IoT) devices with or without infrastructure support. Meanwhile, with the breakthrough of artificial intelligence (AI), more and more AI applications relying on AI methods such as deep neural networks (DNNs) are expected to be applied in various fields, such as smart homes, smart factories, and smart cities, to improve our lifestyles and efficiency dramatically. However, AI applications are generally computation intensive, latency sensitive, and energy consuming, making resource-constrained IoT devices unable to benefit from AI anytime and anywhere. In this article, we study mobile-edge computing (MEC) for AI applications in air–ground-integrated wireless networks. Our goal is to minimize the service latency while ensuring the learning accuracy requirements and energy consumption. To achieve that, we take DNN as the typical AI application and formulate an optimization problem that optimizes the DNN model decision, computation and communication resource allocation, and UAV trajectory control, subject to the energy consumption, latency, computation, and communication resource constraints. Considering the formulated problem is nonconvex, we decompose it into multiple convex subproblems and then alternately solve them till they converge to the desired solution. Simulation results show that the proposed algorithm significantly improves the system performance for AI applications.