无人机辅助边缘计算网络的任务卸载与资源分配联合优化

利用无人机(UAV)作为空中中继节点，构建空地一体化的边缘计算网络，可以有效克服地面环境局限， 拓展网络覆盖范围，为用户提供便利计算服务。该文面向无人机中继辅助的多用户、多服务器边缘计算网络场景， 以最大化任务完成量为目标，研究了无人机部署位置、用户-服务器关联策略、无人机带宽分配的联合优化问题。 由于该问题包含连续与离散变量，故该文综合运用差分进化、粒子群优化等工具，提出了一种基于块坐标下降 (BCD)的次优算法进行求解。所提算法将原问题解耦为3个子问题独立求解，并通过迭代逼近原始问题最优解。 仿真实验表明，所提算法可在满足用户任务时延需求的前提下，最大化系统总任务完成量，优于其他对比算法。

It can effectively overcome the limitations of the ground environment, expand the network coverage and provide users with convenient computing services, through constructing the air-ground integrated edge computing network with Unmanned Aerial Vehicle (UAV) as the relay. In this paper, with the objective of maximizing the task completion amount, the joint optimization problem of UAV deployment, user-server association and bandwidth allocation is investigated in the context of the UAV assisted multi-user and multiserver edge computing network. The formulated joint optimization problem contains both continuous and discrete variables, which makes itself hard to solve. To this end, a Block Coordinated Descent (BCD) based iterative algorithm is proposed in this paper, involving the optimization tools such as differential evolution and particle swarm optimization. The original problem is decomposed into three sub-problems with the proposed algorithm, which can be solved independently. The optimal solution of the original problem can be approached through the iteration among these three subproblems. Simulation results show that the proposed algorithm can greatly increase the amount of completed tasks, which outperforms other benchmark algorithms.