面向通信计算融合的6G网络智能异质资源管理关键技术*

doi:10.12267/j.issn.2096-5931.2026.09.002

信息通信技术与政策 ›› 2026, Vol. 52 ›› Issue (9): 9-19.doi: 10.12267/j.issn.2096-5931.2026.09.002

专题:通信-计算-控制融合创新发展上一篇下一篇

面向通信计算融合的6G网络智能异质资源管理关键技术^*

Key technologies for intelligent heterogeneous resource management in 6G networks with communication-computing convergence

齐彦丽¹, 蔡青², 周一青¹

¹ 中国科学院计算技术研究所处理器芯片全国重点实验室, 北京 100190
² 中国移动通信有限公司研究院, 北京 100053

收稿日期:2026-08-24 出版日期:2026-09-25 发布日期:2026-09-30
通讯作者: 周一青
作者简介:
齐彦丽,中国科学院计算技术研究所处理器芯片全国重点实验室副研究员,主要从事通感算融合资源管理、智能组网等方面的研究工作；
蔡青,中国移动通信有限公司研究院工程师,主要从事6G、通信与AI融合等方面的研究工作
基金资助:
^*国家重点研发计划(2025YFF0514400)

QI Yanli¹, CAI Qing², ZHOU Yiqing¹

¹ State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
² China Mobile Research Institute, Beijing 100053, China

Received:2026-08-24 Online:2026-09-25 Published:2026-09-30
Contact: ZHOU Yiqing

摘要/Abstract

摘要：

随着6G网络向通信与计算深度融合演进,传统依赖速率优化的网络难以满足大带宽、低时延、密集计算等需求。围绕通信计算融合的异质资源协同管理,介绍6G网络架构与资源协同机理,分析计算辅助通信与通信辅助计算两类关键技术,包括语义通信和协同边缘计算。进一步讨论端到端通信效率、多用户通信资源竞争及计算资源竞争等智能资源管理问题,为6G网络资源协同优化提供参考。

关键词: 通信计算融合, 异质资源协同, 语义通信, 协同移动边缘计算

Abstract:

With the evolution of 6G networks toward convergence of communication and computing, conventional networks relying primarily on transmission rate optimization are insufficient to satisfy the requirements of high bandwidth, low latency, and intensive computing. Focusing on coordination of heterogeneous resource in integrated communication and computing networks, this paper introduces the architectures of 6G networks and the mechanisms of resource collaboration, and analyzes two representative paradigms, namely computing-assisted communication and communication-assisted computing, including semantic communication and collaborative mobile edge computing. Furthermore, key issues in intelligent resource management, such as end-to-end communication efficiency, multi-user communication resource competition, and computing resource competition, are discussed, providing insights into resource coordination and optimization for future 6G networks.

Key words: convergence of communication and computing, coordination of heterogeneous resources, semantic communication, collaborative mobile edge computing

中图分类号:

TN929.5

齐彦丽, 蔡青, 周一青. 面向通信计算融合的6G网络智能异质资源管理关键技术^*[J]. 信息通信技术与政策, 2026, 52(9): 9-19.

QI Yanli, CAI Qing, ZHOU Yiqing. Key technologies for intelligent heterogeneous resource management in 6G networks with communication-computing convergence[J]. Information and Communications Technology and Policy, 2026, 52(9): 9-19.

导出引用管理器 EndNote|Ris|BibTeX

链接本文:

http://ictp.caict.ac.cn/CN/10.12267/j.issn.2096-5931.2026.09.002

http://ictp.caict.ac.cn/CN/Y2026/V52/I9/9

图/表 5

图1 通信发展三段论

图2 CoMEC示意图

图3 端到端语义通信服务用例

图4 多用户语义通信服务用例

图5 多用户CoMEC服务用例

参考文献 35

[1]	IMT 2030 (6G) 推进组. 6G典型场景和关键能力白皮书[R]. 2022-07-24.
[2]	Zhang Y, Liu H, Jiao L, et al. To offload or not to offload: an efficient code partition algorithm for mobile cloud computing[C]// 2012 IEEE 1st International Conference on Cloud Networking (CLOUDNET). Paris: IEEE, 2012: 80-86.
[3]	Hou X, Ren Z, Wang J, et al. Reliable computation offloading for edge-computing-enabled software-defined IoV[J]. IEEE Internet of Things Journal, 2020, 7(8): 7097-7111. doi: 10.1109/JIoT.6488907 URL
[4]	Sun Z, Nakhai M R. Distributed mirror-prox optimization for multi-access edge computing[J]. IEEE Transactions on Communications, 2020, 68(5): 3096-3106. doi: 10.1109/TCOMM.26 URL
[5]	Wang J, Hu J, Min G, et al. Fast adaptive task offloading in edge computing based on meta reinforcement learning[J]. IEEE Transactions on Parallel and Distributed Systems, 2020, 32(1): 242-253. doi: 10.1109/TPDS.71 URL
[6]	Gong X, Bai C, Ren S, et al. A survey of compute first networking[C]// 2023 IEEE 23rd International Conference on Communication Technology (ICCT). Wuxi: IEEE, 2023: 688-695.
[7]	姚惠娟, 耿亮, 陆璐, 等. 算力感知网络技术白皮书[R]. 2022-07-01.
[8]	中国移动研究院. 6G无线内生AI架构与技术白皮书[R]. 2022.
[9]	Kekki S, Featherstone W, Fang Y, et al. MEC in 5G networks[R/OL]. 2018-06. https://www.etsi.org/images/files/ETSIWhitePapers/etsi%5Fwp28%5Fmec%5Fin%5F5G%5FFINAL.pdf.
[10]	Luo X, Chen H H, Guo Q. Semantic communications: overview, open issues, and future research directions[J]. IEEE Wireless Communications, 2022, 29(1): 210-219. doi: 10.1109/MWC.101.2100269 URL
[11]	Zhao L, Wu D, Zhou L, et al. Radio resource allocation for integrated sensing, communication, and computation networks[J]. IEEE Transactions on Wireless Communications, 2022, 21(10): 8675-8687. doi: 10.1109/TWC.2022.3168348 URL
[12]	Şahin A, Yang R. A survey on over-the-air computation[J]. IEEE Communications Surveys & Tutorials, 2023, 25(3): 1877-1908.
[13]	Li S, Maddah-Ali M A, Yu Q, et al. A fundamental tradeoff between computation and communication in distributed computing[J]. IEEE Transactions on Information Theory, 2017, 64(1): 109-128. doi: 10.1109/TIT.2017.2756959 URL
[14]	Sultana M, Paul P P, Gavrilova M L. User recognition from social behavior in computer-mediated social context[J]. IEEE Transactions on Human-Machine Systems, 2017, 47(3): 356-367. doi: 10.1109/THMS.2017.2681673 URL
[15]	Qi Y, Zhou Y, Cai Q, et al. Architecture, characteristics, and resource management of integration of sensing, communications, and computing in 6G[J]. IEEE Network, 2024, 38(2): 54-61.
[16]	Shannon C E. A mathematical theory of communication[J]. The Bell System Technical Journal, 1948, 27(3): 379-423. doi: 10.1002/bltj.1948.27.issue-3 URL
[17]	Weaver W. Recent contributions to the mathematical theory of communication[J]. ETC: A Review of General Semantics, 1953: 261-281.
[18]	Vaswani A. Attention is all you need[J]. Advances in Neural Information Processing Systems, 2017, 30: 1-15.
[19]	Bilal K, Khalid O, Erbad A, et al. Potentials, trends, and prospects in edge technologies: fog, cloudlet, mobile edge, and micro data centers[J]. Computer Networks, 2018, 130: 94-120. doi: 10.1016/j.comnet.2017.10.002 URL
[20]	Shaukat U, Ahmed E, Anwar Z, et al. Cloudlet deployment in local wireless networks: motivation, architectures, applications, and open challenges[J]. Journal of Network and Computer Applications, 2016, 62: 18-40. doi: 10.1016/j.jnca.2015.11.009 URL
[21]	Yi S, Li C, Li Q. A survey of fog computing: concepts, applications and issues[C]// Proceedings of the 2015 Workshop on Mobile Big Data. New York: ACM, 2015: 37-42.
[22]	Mao Y, You C, Zhang J, et al. A survey on mobile edge computing: the communication perspective[J]. IEEE Communications Surveys & Tutorials, 2017, 19(4): 2322-2358.
[23]	Ning Z, Kong X, Xia F, et al. Green and sustainable cloud of things: enabling collaborative edge computing[J]. IEEE Communications Magazine, 2018, 57(1): 72-78.
[24]	Wang K, Yin H, Quan W, et al. Enabling collaborative edge computing for software defined vehicular networks[J]. IEEE Network, 2018, 32(5): 112-117.
[25]	Cai Q, Zhou Y, Liu L, et al. Collaboration of heterogeneous edge computing paradigms: how to fill the gap between theory and practice[J]. IEEE Wireless Communications, 2023, 31(1): 110-117.
[26]	Han Y, Li J, Hoang D, et al. An intent-based network virtualization platform for SDN[C]// 2016 12th International Conference on Network and Service Management (CNSM). Laxenburg: ACM, 2016: 353-358.
[27]	Yang S, Li F, Trajanovski S, et al. Recent advances of resource allocation in network function virtualization[J]. IEEE Transactions on Parallel and Distributed Systems, 2020, 32(2): 295-314. doi: 10.1109/TPDS.71 URL
[28]	Wang Y, Gao Z, Zheng D, et al. Transformer-empowered 6G intelligent networks: from massive MIMO processing to semantic communication[J]. IEEE Wireless Communications, 2023, 30(6): 127-135.
[29]	Devlin J, Chang M, Lee K, et al. Bert: pre-training of deep bidirectional transformers for language understanding[C]// Proceedings of NAACL-HLT. Minneapolis: Association for Computational Linguistics, 2019, 1(2): 4171-4186.
[30]	Xie H, Qin Z, Li G Y, et al. Deep learning enabled semantic communication systems[J]. IEEE Transactions on Signal Processing, 2021, 69: 2663-2675. doi: 10.1109/TSP.2021.3071210 URL
[31]	Xie H, Qin Z. A lite distributed semantic communication system for Internet of Things[J]. IEEE Journal on Selected Areas in Communications, 2020, 39(1): 142-153. doi: 10.1109/JSAC.49 URL
[32]	Tran T X, Hajisami A, Pandey P, et al. Collaborative mobile edge computing in 5G networks: new paradigms, scenarios, and challenges[J]. IEEE Communications Magazine, 2017, 55(4): 54-61.
[33]	Sahni Y, Cao J, Yang L, et al. Multi-hop multi-task partial computation offloading in collaborative edge computing[J]. IEEE Transactions on Parallel and Distributed Systems, 2020, 32(5): 1133-1145. doi: 10.1109/TPDS.71 URL
[34]	Feng J, Liu Z, Wu C, et al. AVE: autonomous vehicular edge computing framework with ACO-based scheduling[J]. IEEE Transactions on Vehicular Technology, 2017, 66(12): 10660-10675. doi: 10.1109/TVT.2017.2714704 URL
[35]	Shi J, Du J, Wang J, et al. Priority-aware task offloading in vehicular fog computing based on deep reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 16067-16081. doi: 10.1109/TVT.25 URL

面向通信计算融合的6G网络智能异质资源管理关键技术^*

Key technologies for intelligent heterogeneous resource management in 6G networks with communication-computing convergence

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