大规模无线通信网络移动边缘计算和缓存研究

黄永明; 郑冲; 张征明; 尤肖虎

doi:10.11959/j.issn.1000-436x.2021096

您当前的位置：

首页 >

文章列表页 >

大规模无线通信网络移动边缘计算和缓存研究

专题：面向未来移动网络的大规模组网关键技术 | 更新时间：2024-06-05

- 大规模无线通信网络移动边缘计算和缓存研究
- Research on mobile edge computing and caching in massive wireless communication network
- 通信学报 2021年42卷第4期页码：44-61
- 作者机构：
  
  1. 东南大学信息科学与工程学院，江苏南京 210096
  2. 紫金山实验室，江苏南京 211111
- 作者简介：
  
  [ "黄永明（1977- ），男，江苏吴江人，博士，东南大学教授、博士生导师，主要研究方向为智能 5G/6G 移动通信、毫米波无线通信等。" ]
  [ "郑冲（1994- ），男，湖北荆州人，东南大学博士生，主要研究方向为智能无线通信、移动边缘计算、边缘智能、物联网、深度强化学习和联邦学习等。" ]
  [ "张征明（1994- ），男，安徽阜阳人，东南大学博士生，主要研究方向为无线大数据、机器学习、5G移动网络、无人机辅助通信和资源管理等。" ]
  [ "尤肖虎（1962- ），男，山东济宁人，博士，东南大学教授、博士生导师，主要研究方向为移动通信系统和信号处理及其应用等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61720106003);国家重点研发计划基金资助项目(2018YFB1800801)
- DOI：10.11959/j.issn.1000-436x.2021096
  中图分类号： TN92
- 网络出版日期：2021-04，
  
  纸质出版日期：2021-04-25
- 稿件说明：
移动端阅览
黄永明, 郑冲, 张征明, 等. 大规模无线通信网络移动边缘计算和缓存研究[J]. 通信学报, 2021,42(4):44-61.

Yongming HUANG, Chong ZHENG, Zhengming ZHANG, et al. Research on mobile edge computing and caching in massive wireless communication network[J]. Journal on communications, 2021, 42(4): 44-61.
黄永明, 郑冲, 张征明, 等. 大规模无线通信网络移动边缘计算和缓存研究[J]. 通信学报, 2021,42(4):44-61. DOI： 10.11959/j.issn.1000-436x.2021096.

Yongming HUANG, Chong ZHENG, Zhengming ZHANG, et al. Research on mobile edge computing and caching in massive wireless communication network[J]. Journal on communications, 2021, 42(4): 44-61. DOI： 10.11959/j.issn.1000-436x.2021096.

摘要

面向未来6G移动通信的大规模网络移动边缘计算与缓存技术，首先，介绍了大规模无线网络下移动边缘计算和缓存的架构与原理，并阐释了移动边缘计算和缓存技术在大规模无线网络中的必要性和普适性。接着，从计算卸载、边缘缓存、多维资源分配、用户关联和隐私保护这5个关键问题出发，综述和分析了移动边缘计算和缓存赋能大规模无线网络时会引入的新型关键问题以及对应的解决方案研究，并进一步指出了未来的发展趋势和研究方向。最后，针对隐私保护问题，提出了一种基于联邦学习的隐私保护方案，并通过仿真结果表明所提方案能够同时保护用户数据隐私且改善系统服务质量。

Abstract

For the large-scale network mobile edge computing and caching technology of future 6G mobile communications

firstly

the architectures and principles of mobile edge computing and caching in large-scale wireless networks were introduced

and the necessity and universality were clarified.Then

from the perspective of the five key issues in the mobile edge computing and caching enabled large-scale wireless network

including computing offloading

edge caching

multi-dimensional resource allocation

user association and privacy protection

the recent researches and further pointed out the future development trends and research directions were reviewed and analyzed.Finally

for the privacy preservation issue

a federated learning based privacy-preserving scheme was proposed.Simulation results show that the proposed scheme can simultaneously preserve user privacy and improve the quality of service.

关键词

Keywords

references

YOU X H , WANG C X , HUANG J , et al . Towards 6G wireless communication networks:vision,enabling technologies,and new paradigm shifts [J ] . Science China Information Sciences , 2020 , 64 ( 1 ): 1 - 74 .

CISCO . Cisco visual networking index:global mobile data traffic forecast update,2017–2022 [R ] . Cisco Public Information , 2017 .

HE S W , HUANG W , WANG J H , et al . Cache-enabled coordinated mobile edge network:opportunities and challenges [J ] . IEEE Wireless Communications , 2020 , 27 ( 2 ): 204 - 211 .

GUO H Z , LIU J J , ZHANG J . Computation offloading for multi-access mobile edge computing in ultra-dense networks [J ] . IEEE Communications Magazine , 2018 , 56 ( 8 ): 14 - 19 .

KAMEL M , HAMOUDA W , YOUSSEF A . Ultra-dense networks:a survey [J ] . IEEE Communications Surveys ＆ Tutorials , 2016 , 18 ( 4 ): 2522 - 2545 .

ABBAS N , ZHANG Y , TAHERKORDI A , et al . Mobile edge computing:a survey [J ] . IEEE Internet of Things Journal , 2018 , 5 ( 1 ): 450 - 465 .

谢人超 , 廉晓飞 , 贾庆民 , 等 . 移动边缘计算卸载技术综述 [J ] . 通信学报 , 2018 , 39 ( 11 ): 138 - 155 .

XIE R C , LIAN X F , JIA Q M , et al . Survey on computation offloading in mobile edge computing [J ] . Journal on Communications , 2018 , 39 ( 11 ): 138 - 155 .

ZHANG K , LENG S P , HE Y J , et al . Mobile edge computing and networking for green and low-latency Internet of things [J ] . IEEE Communications Magazine , 2018 , 56 ( 5 ): 39 - 45 .

PAN C H , ELKASHLAN M , WANG J Z , et al . User-centric C-RAN architecture for ultra-dense 5G networks:challenges and methodologies [J ] . IEEE Communications Magazine , 2018 , 56 ( 6 ): 14 - 20 .

TIAN X J , JIA W J . Improved clustering and resource allocation for ultra-dense networks [J ] . China Communications , 2020 , 17 ( 2 ): 220 - 231 .

ETSI . ETSI GS MEC 002.Mobile edge computing (MEC); technical requirements [R ] . ETSI White Paper , 2016 .

代玥玥 . 移动边缘计算中资源管理问题的研究 [D ] . 成都:电子科技大学 , 2019 .

DAI Y Y . Research on resource management problems in mobile edge computing [D ] . Chengdu:University of Electronic Science and Technology of China , 2019 .

ZENG J , SUN J Y , WU B W , et al . Mobile edge communications,computing,and caching (MEC3) technology in the maritime communication network [J ] . China Communications , 2020 , 17 ( 5 ): 223 - 234 .

HE Y , YU F R , ZHAO N , et al . Secure social networks in 5G systems with mobile edge computing,caching,and device-to-device communications [J ] . IEEE Wireless Communications , 2018 , 25 ( 3 ): 103 - 109 .

HABER E E , NGUYEN T M , ASSI C , et al . An energy-efficient task offloading solution for MEC-based IoT in Ultra-dense networks [C ] // 2019 IEEE Wireless Communications and Networking Conference . Piscataway:IEEE Press , 2019 : 1 - 7 .

YOU C S , HUANG K B . Multiuser resource allocation for mobile-edge computation offloading [C ] // 2016 IEEE Global Communications Conference . Piscataway:IEEE Press , 2016 : 1 - 6 .

MAO Y Y , ZHANG J , LETAIEF K B . Dynamic computation offloading for mobile-edge computing with energy harvesting devices [J ] . IEEE Journal on Selected Areas in Communications , 2016 , 34 ( 12 ): 3590 - 3605 .

ZHANG J , HU X P , NING Z L , et al . Energy-latency tradeoff for energy-aware offloading in mobile edge computing networks [J ] . IEEE Internet of Things Journal , 2018 , 5 ( 4 ): 2633 - 2645 .

QIN M , CHENG N , JING Z W , et al . Service-oriented energy-latency tradeoff for IoT task partial offloading in MEC-enhanced multi-RAT networks [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 3 ): 1896 - 1907 .

MADDAH-ALI M A , NIESEN U . Fundamental limits of caching [J ] . IEEE Transactions on Information Theory , 2014 , 60 ( 5 ): 1077 - 1081 .

BORST S C , GUPTA V , WALID A . Distributed caching algorithms for content distribution networks [C ] // IEEE Conference on Information Communications . Piscataway:IEEE Press , 2010 : 1 - 9 .

TAMOOR-UL-HASSAN S , BENNIS M , NARDELLI P H J , et al . Caching in wireless small cell networks:a storage-bandwidth tradeoff [J ] . IEEE Communications Letters , 2016 , 20 ( 6 ): 1175 - 1178 .

ZHU K L , ZHI W T , CHEN X , et al . Socially motivated data caching in ultra-dense small cell networks [J ] . IEEE Network , 2017 , 31 ( 4 ): 42 - 48 .

DAI C , ZHU K , WANG R , et al . Contextual multi-armed bandit for cache-aware decoupled multiple association in UDNs:a deep learning approach [J ] . IEEE Transactions on Cognitive Communications and Networking , 2019 , 5 ( 4 ): 1046 - 1059 .

TANG J , DAI T W , CUI M M , et al . Optimization for maximizing sum secrecy rate in SWIPT-enabled NOMA systems [J ] . IEEE Access , 2018 , 6 : 43440 - 43449 .

ZHANG Z M , YANG Y Q , HUA M , et al . Proactive caching for vehicular multi-view 3D video streaming via deep reinforcement learning [J ] . IEEE Transactions on Wireless Communications , 2019 , 18 ( 5 ): 2693 - 2706 .

KARAMCHANDANI N , NIESEN U , MADDAH-ALI M A , et al . Hierarchical coded caching [J ] . IEEE Transactions on Information Theory , 2016 , 62 ( 6 ): 3212 - 3229 .

JI M Y , CAIRE G , MOLISCH A F . Fundamental limits of caching in wireless D2D networks [J ] . IEEE Transactions on Information Theory , 2016 , 62 ( 2 ): 849 - 869 .

YAN Q F , CHENG M Q , TANG X H , et al . On the placement delivery array design for centralized coded caching scheme [J ] . IEEE Transactions on Information Theory , 2017 , 63 ( 9 ): 5821 - 5833 .

ZHANG Z M , CHEN H Y , HUA M , et al . Double coded caching in ultra dense networks:caching and multicast scheduling via deep reinforcement learning [J ] . IEEE Transactions on Communications , 2020 , 68 ( 2 ): 1071 - 1086 .

YANG X , CHEN Z Y , LI K K , et al . Communication-constrained mobile edge computing systems for wireless virtual reality:scheduling and tradeoff [J ] . IEEE Access , 2018 , 6 : 16665 - 16677 .

LIU H , JIA H M , CHEN J Q , et al . Computing resource allocation of mobile edge computing networks based on potential game theory [C ] // 2018 IEEE 4th International Conference on Computer and Communications . Piscataway:IEEE Press , 2018 : 693 - 699 .

NIKOLAOU S , RENESSE R V , SCHIPER N . Proactive cache placement on cooperative client caches for online social networks [J ] . IEEE Transactions on Parallel and Distributed Systems , 2016 , 27 ( 4 ): 1174 - 1186 .

AZIMI S M , SIMEONE O , SENGUPTA A , et al . Online edge caching and wireless delivery in fog-aided networks with dynamic content popularity [J ] . IEEE Journal on Selected Areas in Communications , 2018 , 36 ( 6 ): 1189 - 1202 .

WANG F , XU J , CUI S G . Optimal energy allocation and task offloading policy for wireless powered mobile edge computing systems [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 4 ): 2443 - 2459 .

WANG F , XING H , XU J . Real-time resource allocation for wireless powered multiuser mobile edge computing with energy and task causality [J ] . IEEE Transactions on Communications , 2020 , 68 ( 11 ): 7140 - 7155 .

CHEN M Z , SAAD W , YIN C C , et al . Data correlation-aware resource management in wireless virtual reality (VR):an echo state transfer learning approach [J ] . IEEE Transactions on Communications , 2019 , 67 ( 6 ): 4267 - 4280 .

DIAO X B , ZHENG J C , WU Y , et al . Joint computing resource,power,and channel allocations for D2D-assisted and NOMA-based mobile edge computing [J ] . IEEE Access , 2019 , 7 : 9243 - 9257 .

HO T M , NGUYEN K K . Joint server selection,cooperative offloading and handover in multi-access edge computing wireless network:A deep reinforcement learning approach [J ] . IEEE Transactions on Mobile Computing , 2020 , PP ( 99 ): 1536 - 1233 .

THANANJEYAN S , CHAN C A , WONG E , et al . Mobility-aware energy optimization in hosts selection for computation offloading in multi-access edge computing [J ] . IEEE Open Journal of the Communications Society , 2020 , 1 : 1056 - 1065 .

MA S Y , SONG S D , ZHAO J M , et al . Joint network selection and service placement based on particle swarm optimization for multi-access edge computing [J ] . IEEE Access , 2020 , 8 : 160871 - 160881 .

ZHAO J M , WU W J , GUO X , et al . Access selection considering mobile edge computing in ultra dense network [C ] // 2017 3rd IEEE International Conference on Computer and Communications . Piscataway:IEEE Press , 2017 : 433 - 437 .

GAO Y , WU W J , ZHOU T Q , et al . QoE-aware access node selection considering mobile edge computing [C ] // 2018 IEEE 4th International Conference on Computer and Communications . Piscataway:IEEE Press , 2018 : 1914 - 1918 .

SAAD W , HAN Z , DEBBAH M , et al . Coalitional game theory for communication networks [J ] . IEEE Signal Processing Magazine , 2009 , 26 ( 5 ): 77 - 97 .

ZHENG C , LIU S H , HUANG Y M , et al . MEC-enabled wireless VR video service:a learning-based mixed strategy for energy-latency tradeoff [C ] // 2020 IEEE Wireless Communications and Networking Conference . Piscataway:IEEE Press , 2020 : 1 - 6 .

ABDILDIN Y G , ABBAS A E . An algorithm for excluding redundant assessments in a multiattribute utility problem [J ] . Procedia Computer Science , 2012 , 9 ( 1 ): 802 - 811 .

张佳乐 , 赵彦超 , 陈兵 , 等 . 边缘计算数据安全与隐私保护研究综述 [J ] . 通信学报 , 2018 , 39 ( 3 ): 1 - 21 .

ZHANG J L , ZHAO Y C , CHEN B , et al . Survey on data security and privacy-preserving for the research of edge computing [J ] . Journal on Communications , 2018 , 39 ( 3 ): 1 - 21 .

TIAN Z H , WANG Y H , SUN Y B , et al . Location privacy challenges in mobile edge computing:classification and exploration [J ] . IEEE Network , 2020 , 34 ( 2 ): 52 - 56 .

WANG W X , GE S X , ZHOU X B . Location-privacy-aware service migration in mobile edge computing [C ] // 2020 IEEE Wireless Communications and Networking Conference . Piscataway:IEEE Press , 2020 : 1 - 6 .

HE T , CIFTCIOGLU E N , WANG S Q , et al . Location privacy in mobile edge clouds:a chaff-based approach [J ] . IEEE Journal on Selected Areas in Communications , 2017 , 35 ( 11 ): 2625 - 2636 .

WU Q , CHEN X , ZHOU Z , et al . Mobile social data learning for user-centric location prediction with application in mobile edge service migration [J ] . IEEE Internet of Things Journal , 2019 , 6 ( 5 ): 7737 - 7747 .

DAI Y Y , XU D , ZHANG K , et al . Deep reinforcement learning and permissioned blockchain for content caching in vehicular edge computing and networks [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 4 ): 4312 - 4324 .

XU Q C , SU Z , ZHENG Q H , et al . Game theoretical secure caching scheme in multihoming edge computing-enabled heterogeneous networks [J ] . IEEE Internet of Things Journal , 2019 , 6 ( 3 ): 4536 - 4546 .

PASUPULETI S K , RAMALINGAM S , BUYYA R . An efficient and secure privacy-preserving approach for outsourced data of resource constrained mobile devices in cloud computing [J ] . Journal of Network and Computer Applications , 2016 , 64 : 12 - 22 .

BAHRAMI M , SINGHAL M . A light-weight permutation based method for data privacy in mobile cloud computing [C ] // 2015 3rd IEEE International Conference on Mobile Cloud Computing,Services,and Engineering . Piscataway:IEEE Press , 2015 : 189 - 198 .

KRIZHEVSKY A , HINTON G . Learning multiple layers of features from tiny images [R ] . Handbook of Systemic Autoimmune Diseases ,(2009-01-04)[2021-01-21 ] .

KAMP M , ADILOVA L , SICKING J , et al . Efficient decentralized deep learning by dynamic model averaging [C ] // Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Berlin:Springer , 2018 : 393 - 409 .

HE K M , ZHANG X Y , REN S Q , et al . Deep residual learning for image recognition [C ] // 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2016 : 770 - 778 .

浏览量

1755

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

空天地一体化网络中基于联邦深度强化学习的边缘协作缓存策略

移动边缘计算场景下针对资源竞争的服务迁移优化方法

移动边缘计算中基于图到序列深度强化学习的复杂任务部署策略

无线供能智能反射面辅助移动边缘计算系统设计与优化

基于平衡生成对抗网络的海洋气象传感网入侵检测研究