基于移动边缘计算的空天地一体化网络架构

吴健; 贾敏; 郭庆

doi:10.11959/j.issn.2096-8930.2024003

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基于移动边缘计算的空天地一体化网络架构

专题：卫星互联网体系架构及相关技术 | 更新时间：2024-06-03

- 基于移动边缘计算的空天地一体化网络架构
- Space-Air-Ground Integrated Network Architecture Based on Mobile Edge Computing
- 天地一体化信息网络 2024年5卷第1期页码：24-31
- 作者机构：
- 作者简介：
  
  [ "吴健（1999− ），男，哈尔滨工业大学博士生，主要研究方向为卫星边缘计算等" ]
  [ "贾敏（1982− ），女，哈尔滨工业大学教授，博士生导师，主要研究方向为天地一体化认知通信、组网与接入技术等" ]
  [ "郭庆（1964− ），男，哈尔滨工业大学教授、博士生导师，主要研究方向为卫星通信和宽带多媒体通信" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(62231012);国家重点研发计划;The National Key Research and Development Program of China(2021YFB2900500);黑龙江省优秀青年科学基金;The Natural Science Foundation for Outstanding Young Scholars of Heilongjiang Province(YQ2020F001)
- DOI：10.11959/j.issn.2096-8930.2024003
  中图分类号： TP393
- 网络首发：2024-03，
  
  纸质出版：2024-03-20
- 稿件说明：
移动端阅览
吴健, 贾敏, 郭庆. 基于移动边缘计算的空天地一体化网络架构[J]. 天地一体化信息网络, 2024,5(1):24-31.

Jian WU, Min JIA, Qing GUO. Space-Air-Ground Integrated Network Architecture Based on Mobile Edge Computing[J]. Space-Integrated-Ground Information Networks, 2024, 5(1): 24-31.
吴健, 贾敏, 郭庆. 基于移动边缘计算的空天地一体化网络架构[J]. 天地一体化信息网络, 2024,5(1):24-31. DOI： 10.11959/j.issn.2096-8930.2024003.

Jian WU, Min JIA, Qing GUO. Space-Air-Ground Integrated Network Architecture Based on Mobile Edge Computing[J]. Space-Integrated-Ground Information Networks, 2024, 5(1): 24-31. DOI： 10.11959/j.issn.2096-8930.2024003.

摘要

下一代无线通信技术将通过支持智能交通、智能医疗、虚拟现实/增强等服务来提高用户的体验质量。这些新兴的服务通常都是计算密集型和时延敏感型的，必须满足严格的时延、能耗和可靠性要求，而基于云计算的服务难以满足这些要求。为了解决以上问题，提出移动边缘计算（Mobile Edge Computing

MEC）技术，解决将卸载请求回传到云计算中心所具有的高时延和高带宽消耗的问题。空天地一体化网络（Space-Air-Ground Integrated Network

SAGIN）作为6G的重要研究方向，可以弥补全球范围内的巨大覆盖缺口，受到广泛关注。通过将MEC技术、联邦学习技术和人工智能技术引入SAGIN，高效管理网络中海量、异构的资源，构建低时延、低能耗、高可靠性的SAGIN，以支持新兴的各种服务。

Abstract

The next generation of wireless communication technologies will enhance the quality of user experience by supporting services such as intelligent transportation

intelligent healthcare

virtual/augmented reality

and more.These emerging services are typically computation-intensive and delay-sensitive

and must meet stringent delay

energy consumption

and reliability requirements that cloud-based services struggle to meet.In order to solve the above problems

mobile edge computing (MEC) technology was proposed to solve the problems of high latency and high bandwidth consumption when sending offloading requests back to the cloud computing centers.As an important research direction of 6G

space-air-ground integrated network (SAGIN) could make up for the huge coverage gap in the world

and had been widely concerned.This paper introduced MEC technology

federated learning technology and artificial intelligence technology into SAGIN to efficiently manage massive and heterogeneous resources in the network

and builded low-delay

low-energy consumption and high-reliability SAGIN to support various emerging services.

关键词

Keywords

references

ZHANG J , SHEN Y , WANG Y , et al . Dual-timescale resource allocation for collaborative service caching and computation offloading in IoT systems [J ] . IEEE Transactions on Industrial Informatics , 2022 , 19 ( 2 ): 1735 - 1746 .

MOHAMMADI R , AKLEYLEK S , GHAFFARI A , et al . Automatic delay-sensitive applications quality of service improvement with deep flows discrimination in software defined networks [J ] . Cluster Computing , 2023 , 26 ( 1 ): 437 - 459 .

SHUKLA S , HASSAN M F , TRAN D C , et al . Improving latency in Internet-of-Things and cloud computing for real-time data transmission:a systematic literature review (SLR) [J ] . Cluster Computing , 2023 , 26 ( 5 ): 2657 - 2680 .

HU Y C , PATEL M , SABELLA D , et al . Mobile edge computing—A key technology towards 5G [J ] . ETSI White Paper , 2015 , 11 ( 11 ): 1 - 16 .

MAHENGE M P J , LI C L , SANGA C A . Energy-efficient task offloading strategy in mobile edge computing for resource-intensive mobile applications [J ] . Digital Communications and Networks , 2022 , 8 ( 6 ): 1048 - 1058 .

LYU F , XU W C , YUAN Q , et al . Space-air-ground integrated networks for future IoT:architecture,management,service and performance [J ] . Peer-to-Peer Networking and Applications , 2021 , 14 ( 5 ): 3265 - 3267 .

ZHAO M , CHEN C , LIU L , et al . Orbital collaborative learning in 6G space-air-ground integrated networks [J ] . Neurocomputing , 2022 , 497 : 94 - 109 .

CAO K , HU S Y , SHI Y , et al . A survey on edge and edge-cloud computing assisted cyber-physical systems [J ] . IEEE Transactions on Industrial Informatics , 2021 , 17 ( 11 ): 7806 - 7819 .

ZHAO T T , HE L J , HUANG X Y , et al . QoE-driven secure video transmission in cloud-edge collaborative networks [J ] . IEEE Transactions on Vehicular Technology , 2022 , 71 ( 1 ): 681 - 696 .

YU S , GONG X W , SHI Q , et al . EC-SAGINs:edge-computingenhanced space–air–ground-integrated networks for Internet of vehicles [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 8 ): 5742 - 5754 .

LIU Y , NI L Q , PENG M G . A secure and efficient authentication protocol for satellite-terrestrial networks [J ] . IEEE Internet of Things Journal , 2022 , 10 ( 7 ): 5810 - 5822 .

ZHU X M , JIANG C X . Integrated satellite-terrestrial networks toward 6G:architectures,applications,and challenges [J ] . IEEE Internet of Things Journal , 2021 , 9 ( 1 ): 437 - 461 .

ZHANG Y R , CHEN C , LIU L , et al . Aerial edge computing on orbit:a task offloading and allocation scheme [J ] . IEEE Transactions on Network Science and Engineering , 2022 , 10 ( 1 ): 275 - 285 .

DING C F , WANG J B , ZHANG H , et al . Joint optimization of transmission and computation resources for satellite and high altitude platform assisted edge computing [J ] . IEEE Transactions on Wireless Communications , 2021 , 21 ( 2 ): 1362 - 1377 .

CAO X , YANG B , SHEN Y , et al . Edge-assisted multi-layer offloading optimization of LEO satellite-terrestrial integrated networks [J ] . IEEE Journal on Selected Areas in Communications , 2022 , 41 ( 2 ): 381 - 398 .

CHAI F R , ZHANG Q , YAO H P , et al . Joint multi-task offloading and resource allocation for mobile edge computing systems in satellite IoT [J ] . IEEE Transactions on Vehicular Technology , 2023 , 72 ( 6 ): 7783 - 7795 .

TANG Q Q , FEI Z S , LI B , et al . Computation offloading in LEO satellite networks with hybrid cloud and edge computing [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 11 ): 9164 - 9176 .

LIU Y , JIANG L , QI Q , et al . Energy-efficient space–air–ground integrated edge computing for Internet of remote things:a federated DRL approach [J ] . IEEE Internet of Things Journal , 2022 , 10 ( 6 ): 4845 - 4856 .

CHEN Q , MENG W X , QUEK T Q S , et al . Multi-tier hybrid offloading for computation-aware IoT applications in civil aircraft-augmented SAGIN [J ] . IEEE Journal on Selected Areas in Communications , 2022 , 41 ( 2 ): 399 - 417 .

ZHOU H , JIANG K , LIU X X , et al . Deep reinforcement learning for energy-efficient computation offloading in mobile-edge computing [J ] . IEEE Internet of Things Journal , 2021 , 9 ( 2 ): 1517 - 1530 .

BI S Z , HUANG L , WANG H , et al . Lyapunov-guided deep reinforcement learning for stable online computation offloading in mobile-edge computing networks [J ] . IEEE Transactions on Wireless Communications , 2021 , 20 ( 11 ): 7519 - 7537 .

TUONG V D , TRUONG T P , NGUYEN T V , et al . Partial computation offloading in NOMA-assisted mobile-edge computing systems using deep reinforcement learning [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 17 ): 13196 - 13208 .

ZHOU T Y , LI X H , PAN C Y , et al . Multi-server federated edge learning for low power consumption wireless resource allocation based on user QoE [J ] . Journal of Communications and Networks , 2021 , 23 ( 6 ): 463 - 472 .

BATTILORO C , DI LORENZO P , MERLUZZI M , et al . Lyapunov-based optimization of edge resources for energy-efficient adaptive federated learning [J ] . IEEE Transactions on Green Communications and Networking , 2022 , 7 ( 1 ): 265 - 280 .

WANG S Q , TUOR T , SALONIDIS T , et al . Adaptive federated learning in resource constrained edge computing systems [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 6 ): 1205 - 1221 .

ZAHRA S R , CHISHTI M A . Smart cities pilot projects:an IoT perspective [M ] . Cham : Springer , 2021 .

YANG T T , QIN M , CHENG N , et al . Liquid software-based edge intelligence for future 6G networks [J ] . IEEE Network , 2022 , 36 ( 1 ): 69 - 75 .

RATNAYAKE H , CHEN L , DING X F . A review of federated learning:taxonomy,privacy and future directions [J ] . Journal of Intelligent Information Systems , 2023 , 61 ( 3 ): 923 - 949 .

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