This study presents a mobile edge computing (MEC)-enabled UAV communication system, where a number of UAVs are served by terrestrial base stations (TBSs) equipped with computation resource in the non-orthogonal multiple access manner. Each UAV has to offload its computing tasks to the proper TBS due to the limited energy supply. For this, the authors aim at minimising the sum of transmission energy of UAVs and computation energy of TBSs through jointly optimising the UAV transmit power, computation resource allocation, and UAV grouping. Considering the non-convexity of this optimisation problem, they obtain the optimal solution in the coupled steps: the convex resource allocation optimisation and the combinatorial UAV grouping optimisation. By exploiting the convex nature of the resource allocation optimisation problem, they obtain the optimal transmit power and computation allocation based on the KKT conditions and the idea of gradient descent method when considering a single TBS. Then, they adopt the simulated annealing to obtain the optimal UAV grouping and TBS selection based on the proposed resource allocation optimisation algorithm. Finally, simulation results show that the proposed joint optimisation of transmit power, computation resource allocation, and UAV grouping can effectively reduce the energy consumption of MEC-aware UAV communication system.

中文翻译：

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启用MEC的无人机通信网络中无人机分组和能耗的联合优化

这项研究提出了一种支持移动边缘计算（MEC）的UAV通信系统，其中许多UAV由配备有计算资源的地面基站（TBS）以非正交多址方式提供服务。由于能量供应有限，每个无人机必须将其计算任务转移到适当的TBS。为此，作者旨在通过共同优化无人机的发射功率，计算资源分配和无人机分组来最大程度地降低无人机的传输能量和TBS的计算能量之和。考虑到该优化问题的非凸性，他们在耦合步骤中获得了最优解：凸资源分配优化和组合式UAV分组优化。通过利用资源分配优化问题的凸性，当考虑单个TBS时，它们基于KKT条件和梯度下降方法的思想获得最佳发射功率和计算分配。然后，他们基于提出的资源分配优化算法，采用模拟退火算法来获得最优的无人机分组和TBS选择。最后，仿真结果表明，所提出的发射功率，计算资源分配和UAV分组的联合优化可以有效地降低感知MEC的UAV通信系统的能耗。