加权能耗最小化的无人机辅助移动边缘计算资源分配策略

针对无人机(UAV)辅助的移动边缘计算(MEC)系统，考虑到无人机能耗与地面设备能耗不在一个数量级，该文提出通过给地面设备能耗增加一个权重因子以平衡无人机能耗与地面设备能耗。同时在满足地面设备的任务需求下，通过联合优化无人机轨迹、系统资源分配以最小化无人机和地面设备的加权能耗。该问题高度非凸，为此提出一个基于交替优化算法的两阶段资源分配策略解决该非凸问题。第1阶段在给定地面设备的卸载功率下，利用连续凸逼近(SCA)方法求解无人机轨迹规划、CPU频率资源分配及卸载时间分配；第2阶段求解地面设备的卸载功率分配。通过两阶段的交替和迭代优化找到原问题的次优解。仿真结果验证了所提算法在降低系统能耗方面的有效性。

Resource Allocation for Unmanned Aerial Vehicle-assisted Mobile Edge Computing to Minimize Weighted Energy Consumption

For the Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) system, it is proposed to balance the energy consumption of UAV and ground equipment by adding a weight factor to the energy consumption of ground equipment, considering that their energy consumption is not in the same order of magnitude. At the same time, to meet the requirements of ground equipment tasks, the weighted energy consumption of UAV and ground equipment are minimized by joint optimization of UAV trajectory and system resource allocation. The formulated problem is highly non-convex, thus an alternating optimization based two-stage resource allocation optimization scheme is proposed to solve it. In the first stage, given the unloading power of the ground equipment, the Successive Convex Approximation (SCA) method is used to solve the UAV trajectory optimization, Central Processing Unit (CPU) frequency resource allocation and unloading time allocation. In the second stage, the unloading power allocation of ground equipment is optimized. Such two-stage alternating and iterative optimization is used to find the sub-optimal solution of the original problem. The effectiveness of the proposed scheme in reducing system energy consumption is verified by simulation results.