融合MEC的星地协同网络：架构、关键技术与挑战

doi:10.11959/j.issn.1000-436x.2020082

Abstract

Abstract:

The introduction of mobile edge computing (MEC) technology in satellite-terrestrial networks can effectively improve the quality of user experience and reduce network redundant traffic,but also brings some challenges.Firstly,the basic architecture of satellite-terrestrial networks and MEC technology was introduced.Moreover,the motivation of introducing MEC into satellite-terrestrial networks and the deployment of MEC were discussed.Then,the architecture of MEC enabled satellite-terrestrial networks was proposed,and the key techniques and typical applications were summarized and analyzed.Finally,the key challenges such as task scheduling and mobility management and some open research issues in integrated networks were summarized.It hopes to provide new ideas for future research in this field.

Key words: satellite-terrestrial network, edge computing, task scheduling, mobility management

CLC Number:

TP393

Qinqin TANG,Renchao XIE,Xu LIU,Yasheng ZHANG,Ci HE,Chengcheng LI,Tao HUANG. MEC enabled satellite-terrestrial network:architecture,key technique and challenge[J]. Journal on Communications, 2020, 41(4): 162-181.

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References 61

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比较项目	种类	高度/km	时延/ms	移动性	路由	传输技术	通信协议	计算效率	覆盖范围	通信代价	发展时间	环境	优势	劣势
	LEO	200~3 000	15	快
卫星网络	MEO	3 000	100	较快	动态	激光、微波	DTN	低	大	高	短	太空、航空	服务覆盖范围广、广播/多播	高传输时延、能力受限、高移动性
	GEO	35 786	250	对地静止
地面网络	光纤	无	5	静止	静态	微波、光纤	TCP/IP	高	小	低	长	航空、海洋、陆地	资源丰富、高吞吐量	服务覆盖范围受限、易受自然灾害影响
	5G	无	约1	静止

优化目标	文献	主要贡献	支撑技术	应用场景
以降低时延为优化目标	文献[39]	提出了一种基于空间的云-雾卫星网络切片架构，并结合 SDN/NFV技术设计了一种资源管理机制，以使网络切片更加灵活	网络切片SDN/NFV	5G
	文献[40]	提出了一种卫星物联网边缘智能计算架构，并分析了边缘计算和人工智能在图像数据目标检测中的作用	人工智能	5G/6G、物联网
以降低时延和能耗为优化目标	文献[25]	提出了一种融合MEC的星地协同网络架构以提高用户的QoS，并提出了一种协同任务调度方案以降低用户感知的时延和系统能耗	NFV	5G
	文献[41]	提出了一种面向物联网的星地协同边缘组网架构，并提出了一种联合资源分配和计算卸载方案	云计算	物联网
以提高系统性能为优化目标	文献[42]	提出了一种软件定义的星地协同组网体系结构，并提出了一种基于边缘计算的网络优化方案以提高网络管理灵活性	SDN/NFV	5G
	文献[43]	提出了一种面向智慧城市的星地协同雾计算架构，并设计了一种资源定价的激励方案，以平衡网络资源分配	雾计算	车联网、物联网

场景	优化目标	文献	方案	技术方法
	吞吐量	文献[47]	提出了一个结合了无人机和D2D的网络架构解决网络中的信道分配问题	基于分布式反协调博弈的信道分配算法
星地协同网络		文献[48]	优化无人机航迹	迭代算法
			满足所有用户的速率要求
	信道和聚合数据速率	文献[49]	基于多播子分组最大满意度指数进行度量利用卫星链路进行流量卸载	子分组方法
移动边缘计算	时延	文献[50]	解决了联合计算卸载和单元选择策略问题提出了优化任务执行等待时间的调度方案	搜索方法和 Khun-Munkres算法
移动边缘计算	能耗	文献[51]	提出一种通过采用FiWi接入的网络架构考虑移动终端的时延约束和信道约束	分布式博弈论算法
		文献[52]	优化计算卸载决策和资源分配	基于粒子群算法的层次化遗传算法
			考虑计算卸载决策、功率控制和无线资源分配
融合网络	时延和能耗	文献[25]	LEO对资源进行整合和分配调度用户计算任务卸载	协作计算卸载方法
		文献[41]	提出了一种联合资源分配和任务调度方法设计了基于深度强化学习的计算卸载算法	启发式算法

场景	优化目标	文献	方案	技术方法
	网络可靠性	文献[53]	在地面网络中使用Hata模型，在卫星网络中使用统计阴影模型	自适应神经模糊推理系统的切换算法
星地协同网络		文献[54]	研究位置管理和自动路由维护问题达到 100%路由切换成功率的性能	集中式路由控制算法
星地协同网络	吞吐量和能量	文献[55]	讨论了 UAV 的移动中继技术及信息分发技术，通过利用 D2D通信和UAV移动性实现有效信息分发	—
	时延	文献[56]	考虑到用户无法精准获取网络信息，解决系统信息不确定性导致的移动性管理问题	Q学习算法
移动边缘计算		文献[57]	设计了基于马尔科夫的虚拟机动态迁移策略，提出了利用控制器协助寻找最佳迁移阈值的方案	李雅普诺夫算法
移动边缘计算	计算性能	文献[58]	以用户为中心进行移动性管理，最大化边缘计算性能，保持较低的用户通信能耗	—
融合网络	网络负载均衡	文献[43]	建立了基于车辆移动性的车辆资源地理迁移模型，提出了一种基于资源定价的车辆路径选择激励方案	动态资源迁移算法

MEC enabled satellite-terrestrial network:architecture,key technique and challenge

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