3D Multi-UAV Computing Networks: Computation Capacity and Energy Consumption Tradeoff

Unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) networks have greatly practical significance for improving the computation capacity of ground terminal devices (TDs) in Internet of Things (IoT), while the triggered energy consumption becomes a non-negligible critical issue. In this paper, we propose a novel MEC framework assisted by multiple UAVs. Our goal is to balance the computation capacity and the energy consumption to maximize the network utility, by jointly optimizing power control, UAV-TD pairing, computation resource allocation, and UAVs’ 3D trajectory. In particular, a flexible fairness-aware mechanism is set to maintain the fairness among these TDs. To solve the problem, we characterize the expected offloading rate on the basis of statistical channel information. Then, we propose a joint optimization algorithm based on the successive convex approximation technique and the block coordinate descent structure. The proposed trajectory optimization algorithm caters the UAV-MEC missions as the proposed algorithm is dedicated for solving the constrained UAV 3D path planning problems, and the results can be implemented in practice. Numerical results demonstrate the tradeoff between the computation capacity and the energy consumption, and validate the superiority of the proposed algorithm in the terms of improving the network utility.