基于无人机辅助的移动边缘计算(mobile edge computing,MEC)通过灵活构建的视距链路,能大大提升网络通信质量,在处理计算密集型和延迟敏感任务中发挥重要作用.针对单无人机辅助的MEC存在覆盖范围有限和任务处理时间长的缺陷,对多架载有MEC服务器的无人机为多个地面用户提供计算卸载服务的最小计算比特数最大化问题进行研究.在有限能耗和禁飞区的约束下,对用户调度、用户上传功率、任务卸载与本地计算时间和无人机轨迹进行联合优化,提出一种基于块坐标下降法的迭代优化算法,为用户提供更加公平的计算卸载服务.利用块坐标下降法将原始问题解耦为4个子问题,并采用逐次凸逼近将非凸子问题转化为凸优化子问题进行求解.仿真结果表明,与其他基准方案相比,所提出的联合优化方案能显著提高用户最小计算比特数.
Based on unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC), the flexible construction of line-of-sight links significantly improves the communication quality of a system and plays an important role in handling computationally intensive and latency-sensitive tasks. However, single UAV-assisted MEC suffers from a limited coverage range and long task processing time. This study considers multiple UAVs with MEC servers to provide offloading computing services for multiple ground users, and it presents the problem of maximizing the minimum number of computation bits. Under the constraints of limited energy consumption and no-fly zones, this study jointly optimized user scheduling, user upload power, task offloading time, local computation time, and UAV trajectory. An iterative optimization algorithm based on block coordinate descent was introduced to provide users with a fairer computation offloading service. The original problem is divided into four subproblems, and the non-convex subproblem is transformed into a convex optimization subproblem via successive convex approximations. The simulation results show that compared with other benchmark schemes, the proposed joint optimization scheme can significantly increase the number of maximum-minimum computation bits.
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