UAV-assisted MECResources managementUnmanned aerial vehicles (UAV) have been widely used in various fields because of their high mobility and portability. At the same time, due to the rapid development of artificial intelligence, people's demand for computing is increasing, and the computing power ...
基于NFV和SDN的无人机MEC网络系统 本申请涉及一种基于NFV和SDN的无人机MEC网络系统.所述系统包括物理层,编排器层,虚拟功能网元控制层,NFV虚拟化层和SDN控制器;物理层为整个无人机MEC网络系统的计算和存... 被引量: 0发表: 2024年研究点推荐 高安全卸载能效方法 ...
UAV-enabled fair offloading for MEC networks: a DRL approach based on actor-critic parallel architecturePublished: 29 February 2024 Volume 54, pages 3529–3546, (2024) Cite this article Applied Intelligence Aims and scope Submit manuscript
小化的UAV辅助MEC网络多维资源管控策略.具体内容如下: 针对给定UAV个数的场景,提出基于块坐标下降法的UAV辅助MEC网络多维资源管控策略,通过最小化能耗实现成本最小.首先基于Kmeans++的贪心算法得到问题的一组初始解.然后在每一轮的迭代过程中,依次优化用户与无人机的关联,计算任务的卸载,计算资源分配和UAV位置.具体...
Unmanned Aerial Vehicle (UAV) assisted Mobile Edge Computing (MEC) systems provide substantial benefits for task offloading and communication services, especially in situations where traditional communication infrastructure is unavailable. Current research emphasizes maintaining communication quality while minimizing...
本发明公开了一种IRS和UAV辅助MEC系统中的能耗优化方法,包括建立IRS和UAV辅助的MEC系统,MEC系统中包括携带MEC服务器的UAV、IRS和预设数量的移动用户,在IRS的作用下,移动用户将部分任务卸载到MEC服务器上进行计算。构建所有移动用户在预设时间段的平均能量消耗的最小化目标函数,并将目标函数的优化问题转化为对UAV的飞行...
How-ever, a predominant focus in existing research on UAV deployment centers around optimizing system performance under the consideration of a fixed computing resource pool, resulting in a potential computing bottleneck for delivering large-scale multi-access edge computing (MEC) services. In this ...
无人机辅助MEC系统中基于最优SIC顺序的能耗优化方案 在基于上行非正交多址接入(NOMA)的无人机(UAV)辅助移动边缘计算(MEC)系统中,NOMA的连续干扰消除(SIC)顺序已成为限制上行任务卸载链路传输性能的瓶颈,为降低系统能耗,... 季薇,杨许鑫,李飞,... - 《通信学报》 被引量: 0发表: 2024年 NOMA物联网下多UAV...
However, the limited energy storage, caching capacity, and computation resources of UAVs bring new challenges for UAV-aided MEC, e.g., how to recharge UAVs and how to jointly optimize service-caching, computation-offloading, and UAVs flight trajectories. To overcome the above-mentioned difficulties...
Therefore, this paper combines a UAV and mobile edge computing (MEC), and designs a deep reinforcement learning-based online task offloading (DOTO) algorithm. The algorithm can obtain an online offloading strategy that maximizes the residual energy of the UAV by jointly optimizing the UAV’s time...