关键词: 无人机通信, 移动边缘计算, 凸优化, 连续凸逼近, 自适应聚类

Abstract: In the scenario of using Unmanned Aerial Vehicle (UAV) as the data collector for computation offloading to provide Mobile Edge Computing (MEC) services to User Equipment (UE), a wireless communication strategy to achieve efficient UE coverage through UAV was designed. Firstly, under the condition of a given UE distribution, for the UAV flight trajectory and communication strategy, an optimization method of Successive Convex Approximation (SCA) was used to obtain an approximate optimal solution that was able to minimize the global energy. In addition, for scenarios with large-scale distribution of UEs or a large number of tasks, an adaptive clustering algorithm was proposed to divide the UEs on the ground into as few clusters as possible, and to ensure the offloading data of all UEs in each cluster was able to be collected in one flight. Finally, the computation offloading data collection tasks of the UEs in each cluster were allocated to one flight, so that the goal of reducing the number of dispatches required for a single UAV or the UAV number of dispatches required for multiple UAVs to complete the task was achieved. The simulation results show that the proposed method can generate fewer clusters than the K-Means algorithm and converge quickly, and is suitable for UAV-assisted computation offloading scenarios with widely distributed UEs.

Key words: Unmanned Aerial Vehicle (UAV) communication, Mobile Edge Computing (MEC), convex optimization, Successive Convex Approximation (SCA), adaptive clustering