Communication-Computing Integrated Wireless Networks: From Cloud-Based Collaboration to Integrated Sensing and Intelligent Coordination

Peng Mugen; Jiang Ning; Yan Shi

doi:10.11959/j.issn.1000-436x.TXXB260063

您当前的位置：

首页 >

文章列表页 >

Communication-Computing Integrated Wireless Networks: From Cloud-Based Collaboration to Integrated Sensing and Intelligent Coordination

更新时间：2026-04-14

- Communication-Computing Integrated Wireless Networks: From Cloud-Based Collaboration to Integrated Sensing and Intelligent Coordination
- Journal on Communications (2026)
- 作者机构：
  
  1.北京邮电大学网络与交换技术国家重点实验室，北京 100876
  2.低轨星座融合通信与组网技术北京市重点实验室，北京 100876
- 作者简介：
- 基金信息：
  
  The National Science and Technology Major Project(2024ZD1300100);The National Natural Science Foundation of China(62371067);Beijing Natural Science Foundation(L223007)
- DOI：10.11959/j.issn.1000-436x.TXXB260063
  CLC： TN929.5
- Received：26 January 2026，
  
  Revised：2026-04-14，
  
  Accepted：14 April 2026，
- 稿件说明：
移动端阅览
Peng Mugen, Jiang Ning, Yan Shi. Communication-Computing Integrated Wireless Networks: From Cloud-Based Collaboration to Integrated Sensing and Intelligent Coordination[J/OL]. Journal on Communications, 2026.
DOI：

Peng Mugen, Jiang Ning, Yan Shi. Communication-Computing Integrated Wireless Networks: From Cloud-Based Collaboration to Integrated Sensing and Intelligent Coordination[J/OL]. Journal on Communications, 2026. DOI： 10.11959/j.issn.1000-436x.TXXB260063.

摘要

为了满足巨容量和极低时延性能需求，通信计算融合无线网络通过分解传统基站功能、构建基带池云化集中协同机制，并逐渐向云-边-端一体化的云化协同演进，实现了资源高效复用、信息实时处理与干扰精准抑制。随着各种新兴应用涌现，通信感知一体和AI内生成为6G重要特征，感知和智能计算（简称感智）协同进一步赋能通信计算融合无线网络云-边-端-业，推动云化协同迈向感智协同。本文系统梳理了云化协同学术研究与产业化进展，重点介绍了组网性能规律、体系架构设计及关键技术；提出了感智协同理论体系和关键技术，阐明了感智协同的性能增益，为6G全域覆盖与极致性能需求提供了支撑。结合典型应用场景，展望了感智协同在低空经济、低轨卫星和空天地一体化等领域的应用。

Abstract

To meet the requirements for massive capacity and ultra-low latency in 6G

the evolution of communication-computing integrated wireless networks from cloud-based collaboration to integrated sensing and intelligent coordination was investigated. The academic research and industrial progress of cloud-based collaboration were systematically summarized

with an emphasis on networking performance

system architecture

and key technologies. Subsequently

a theoretical framework and key technologies for integrated sensing and intelligent coordination were proposed. The resulting performance gains were elaborated

and solid support for achieving global coverage and ultimate performance in 6G was provided. Finally

this paper envisions future applications of integrated sensing and intelligent coordination in typical scenarios

such as the low-altitude economy

low-Earth orbit satellites

and space-air-ground integration.

关键词

Keywords

references

尹博南 , 艾元 , 彭木根 . 雾无线接入网：架构、原理和挑战 [J ] . 电信科学 , 2016 , 32 ( 6 ): 20 - 27 .

Yin B N , Ai Y , Peng M G . Fog computing based radio access networks：architecture，principles and challenges [J ] . Telecommunications science , 2016 , 32 ( 6 ): 20 - 27 .

Sun Y H , Peng M G . Recent advances of heterogenous radio access networks: A survey [J ] . Journal of Mobile Multimedia , 2018 , 14 ( 4 ): 345 - 366 .

闫实 , 彭木根 , 王文博 . 通信-感知-计算融合: 6G 愿景与关键技术 [J ] . 北京邮电大学学报 , 2021 , 44 ( 4 ): 1 - 11 .

Yan S , Peng M G , Wang W B . Integration of Communication, Sensing and Computing: the Vision and Key Technologies of 6G [J ] . Journal of Beijing University of Posts and Telecommunications , 2021 , 44 ( 4 ): 1 - 11 .

Brown G . Service-based architecture for 5g core networks [J ] . Huawei White Paper , 2017 , 1 .

李子姝 , 谢人超 , 孙礼 , 等 . 移动边缘计算综述 [J ] . 电信科学 , 2024 , 34 ( 1 ): 87 - 101 .

Li Z S , Xie R C , Sun L , et al . A survey of mobile edge computing [J ] . Telecommunications science , 2018 , 34 ( 1 ): 87 - 101 .

董裕民 , 张静 , 谢昌佐 , 等 . 云边端架构下边缘智能计算关键问题综述: 计算优化与计算卸载 [J ] . 电子与信息学报 , 2024 , 46 ( 3 ): 765 - 776 .

Dong Y M , Zhang J , Xie C Z , et al . A Survey of Key Issues in Edge Intelligent Computing Under Cloud-Edge-Terminal Architecture: Computing Optimization and Computing Offloading [J ] . Journal of Electronics & Information Technology , 2024 , 46 ( 3 ): 765 - 776 .

Peng M G , Yan S , Zhang K C , et al . Fog-computing-based radio access networks: Issues and challenges [J ] . IEEE Network , 2016 , 30 ( 4 ): 46 - 53 .

Xiang H Y , Yan S , Peng M G . A realization of fog-RAN slicing via deep reinforcement learning [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 4 ): 2515 - 2527 .

张平 , 牛凯 , 田辉 , 等 . 6G移动通信技术展望 [J ] . 通信学报 , 2019 , 40 ( 01 ): 141 - 148 .

Zhang P , Niu K , Tian H , et al . Technology prospect of 6G mobile communications [J ] . Journal on Communications , 2019 , 40 ( 1 ): 141 - 148 .

彭木根 , 孙耀华 , 王文博 . 智简 6G 无线接入网: 架构, 技术和展望 [J ] . 北京邮电大学学报 , 2020 , 43 ( 3 ): 1 - 12 .

Peng M G , Sun Y H , Wang W B . Intelligent-Concise Radio Access Networks in 6G: Architecture, Techniques and Insight [J ] . Journal of Beijing University of Posts and Telecommunications , 2020 , 43 ( 3 ): 1 - 10 .

Peng M G , Sun Y H , Li X L , et al . Recent advances in cloud radio access networks: System architectures, key techniques, and open issues [J ] . IEEE Communications Surveys & Tutorials , 2016 , 18 ( 3 ): 2282 - 2308 .

Peng M G , Yan S , Poor H V . Ergodic capacity analysis of remote radio head associations in cloud radio access networks [J ] . IEEE Wireless Communications Letters , 2014 , 3 ( 4 ): 365 - 368 .

Wu D P , Negi R . Effective capacity: a wireless link model for support of quality of service [J ] . IEEE Transactions on wireless communications , 2003 , 2 ( 4 ): 630 - 643 .

Zhao Z Y , Peng M G , Ding Z G , et al . Cluster content caching: An energy-efficient approach to improve quality of service in cloud radio access networks [J ] . IEEE Journal on Selected Areas in Communications , 2016 , 34 ( 5 ): 1207 - 1221 .

Yin B N , Peng M G , Yan S , et al . Tradeoff between ergodic rate and delivery latency in fog radio access networks [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 4 ): 2240 - 2251 .

Peng M G , Yu Y L , Xiang H Y , et al . Energy-efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks [J ] . IEEE transactions on Multimedia , 2016 , 18 ( 5 ): 879 - 892 .

Zhao Z Y , Bu S Q , Zhao T Z , et al . On the design of computation offloading in fog radio access networks [J ] . IEEE Transactions on Vehicular Technology , 2019 , 68 ( 7 ): 7136 - 7149 .

张平 , 许晓东 , 韩书君 , 等 . 智简无线网络赋能行业应用 [J ] . 北京邮电大学学报 , 2020 , 43 ( 06 ): 1 - 9 .

Zhang P , Xu X D , Han S J , et al . Entropy Reduced Mobile Networks Empowering Industrial Applications [J ] . Journal of Beijing University of Posts and Telecommunications , 2020 , 43 ( 6 ): 1 - 9 .

Zhang P , Xu W J , Liu Y M , et al . Intellicise wireless networks from semantic communications: A survey, research issues, and challenges [J ] . IEEE Communications Surveys & Tutorials , 2024 .

Li J , Peng M G , Yu Y L , et al . Energy-efficient joint congestion control and resource optimization in heterogeneous cloud radio access networks [J ] . IEEE Transactions on Vehicular Technology , 2016 , 65 ( 12 ): 9873 - 9887 .

Peng M G , Zhang K C , Jiang J M , et al . Energy-efficient resource assignment and power allocation in heterogeneous cloud radio access networks [J ] . IEEE Transactions on Vehicular Technology , 2014 , 64 ( 11 ): 5275 - 5287 .

Ai Y , Peng M G , Zhang K C . Edge computing technologies for Internet of Things: a primer [J ] . Digital Communications and Networks , 2018 , 4 ( 2 ): 77 - 86 .

Mao Y Y , You C S , Zhang J , et al . A survey on mobile edge computing: The communication perspective [J ] . IEEE communications surveys & tutorials , 2017 , 19 ( 4 ): 2322 - 2358 .

Mach P , Becvar Z . Mobile edge computing: A survey on architecture and computation offloading [J ] . IEEE communications surveys & tutorials , 2017 , 19 ( 3 ): 1628 - 1656 .

Lin P , Song Q Y , Jamalipour A . Multidimensional cooperative caching in CoMP-integrated ultra-dense cellular networks [J ] . IEEE Transactions on Wireless Communications , 2019 , 19 ( 3 ): 1977 - 1989 .

项弘禹 , 肖扬文 , 张贤 , 等 . 5G 边缘计算和网络切片技术 [J ] . 电信科学 , 2024 , 33 ( 6 ): 54 - 63 .

Xiang H Y , Xiao Y W , Zhang X , et al . Edge computing and network slicing technology in 5G [J ] . Telecommunications science , 2017 , 33 ( 6 ): 54 - 63 .

Rahman G M S , Peng M G , Zhang K C , et al . Radio resource allocation for achieving ultra-low latency in fog radio access networks [J ] . IEEE Access , 2018 , 6 : 17442 - 17454 .

Peng M G , Zhao Z Y , Sun Y H . Computation Offloading in Fog Radio Access Networks [M ] // Fog Radio Access Networks (F-RAN) Architectures, Technologies, and Applications . Cham : Springer International Publishing , 2020 : 153 - 178 .

Zhang J P , Yan S , Peng M G . Joint beam alignment and resource allocation for multi-user mmwave integrated sensing and communication systems [J ] . IEEE Transactions on Vehicular Technology , 2023 , 73 ( 4 ): 5288 - 5303 .

Dong F W , Liu F , Cui Y H , et al . Sensing as a service in 6G perceptive networks: A unified framework for ISAC resource allocation [J ] . IEEE Transactions on Wireless Communications , 2022 , 22 ( 5 ): 3522 - 3536 .

Peng M G , Zhang J P , Yan S , et al . Integrated Communication, Sensing and Computing Enabled Fog Radio Access Networks: Issues and Challenges [J ] . IEEE Network , 2025 .

Peng M G , Quek T Q S , Mao G Q , et al . Artificial-intelligence-driven fog radio access networks: Recent advances and future trends [J ] . IEEE Wireless Communications , 2020 , 27 ( 2 ): 12 - 13 .

Zhang X , Peng M G . Testbed design and performance emulation in fog radio access networks [J ] . IEEE Network , 2019 , 33 ( 3 ): 49 - 57 .

Liu A , Li M , Kobayashi M , et al . Fundamental limits for ISAC: Information and communication theoretic perspective [M ] // Integrated Sensing and Communications . Singapore : Springer Nature Singapore , 2023 : 23 - 52 .

Wubben D , Rost P , Bartelt J S , et al . Benefits and impact of cloud computing on 5G signal processing: Flexible centralization through cloud-RAN [J ] . IEEE signal processing magazine , 2014 , 31 ( 6 ): 35 - 44 .

Peng M G , Zhao Z Y , Sun Y H . Cooperative Signal Processing in Fog Radio Access Networks [M ] // Fog Radio Access Networks (F-RAN) Architectures, Technologies, and Applications . Cham : Springer International Publishing , 2020 : 61 - 84 .

Lien S Y , Hung S C , Chen K C , et al . Ultra-low-latency ubiquitous connections in heterogeneous cloud radio access networks [J ] . IEEE Wireless Communications , 2015 , 22 ( 3 ): 22 - 31 .

Shi Y M , Zhang J , Letaief K B , et al . Large-scale convex optimization for ultra-dense cloud-RAN [J ] . IEEE Wireless Communications , 2015 , 22 ( 3 ): 84 - 91 .

Jiang N , Yan S , Liu H R , et al . Computation Offloading for Distributed Learning in Vehicular Networks: A Service Scheduling and Resource Allocation Method [J ] . IEEE Transactions on Vehicular Technology , 2025 .

Yan S , Peng M , Cao X Y . A game theory approach for joint access selection and resource allocation in UAV assisted IoT communication networks [J ] . IEEE Internet of Things Journal , 2018 , 6 ( 2 ): 1663 - 1674 .

Yan S , Jiao M H , Zhou Y C , et al . Machine-learning approach for user association and content placement in fog radio access networks [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 10 ): 9413 - 9425 .

Yan S , Qi L , Zhou Y C , et al . Joint user access mode selection and content popularity prediction in non-orthogonal multiple access-based F-RANs [J ] . IEEE Transactions on Communications , 2019 , 68 ( 1 ): 654 - 666 .

Sun Y H , Chen J M , Wang Z Y , et al . Enabling mobile virtual reality with open 5G, fog computing and reinforcement learning [J ] . IEEE Network , 2022 , 36 ( 6 ): 142 - 149 .

Dang T , Peng M G . Joint radio communication, caching, and computing design for mobile virtual reality delivery in fog radio access networks [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 7 ): 1594 - 1607 .

Yuan S , Peng M G , Sun Y H . Satellite-Terrestrial Integrated Fog Networks: Architecture, Technologies, and Challenges [J ] . IEEE Wireless Communications , 2025 .

彭木根 , 袁硕 . 面向星地融合的 6G 云雾化自组网 [J ] . 电信科学 , 2024 , 40 ( 3 ): 1 - 14 .

Peng M G , Yuan S . Toward satellite-terrestrial integration: 6G cloud-fog collaborative self-organizing network [J ] . Telecommunications Science , 2024 , 40 ( 03 ): 1 - 14 .

Liu B H , Liu C X , Peng M G . Dynamic cache placement and trajectory design for UAV-assisted networks: A two-timescale deep reinforcement learning approach [J ] . IEEE Transactions on Vehicular Technology , 2023 , 73 ( 4 ): 5516 - 5530 .

Sun Y H , Peng M G , Mao S W , et al . Hierarchical radio resource allocation for network slicing in fog radio access networks [J ] . IEEE Transactions on Vehicular Technology , 2019 , 68 ( 4 ): 3866 - 3881 .

Xiang H Y , Peng M G , Sun Y H , et al . Mode selection and resource allocation in sliced fog radio access networks: A reinforcement learning approach [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 4 ): 4271 - 4284 .

Zhang Y M , Zhou J E , Zhu M , et al . Resource Balance Optimization of Network Slicing Based on MDW-SFCM in Space-Air-Ground Integrated Networks [C ] // Proceedings of the 2025 IEEE/CIC International Conference on Communications in China (ICCC Workshops) . IEEE , 2025 : 1 - 6 .

张彤 , 任奕璟 , 闫实 , 等 . 人工智能驱动的 6G 网络: 智慧内生 [J ] . 电信科学 , 2020 , 36 ( 9 ).

Zhang T , Ren Y J , Yan S , et al . Artificial intelligence driven 6G networks:endogenous intelligence [J ] . Telecommunications science , 2020 , 36 ( 9 ): 14 - 22 .

Liatsas L , Kibalya G M , Antonopoulos A . XAI-driven model design for resource utilization forecasting in cloud-native 6G networks [C ] // Proceedings of the 2024 IEEE International Mediterranean Conference on Communications and Networking (MeditCom) . IEEE , 2024 : 566 - 571 .

Zhao M K , Li X , Huang Y S , et al . IC2S-Swarm: When Digital Twin Meets Collaborative ISR [J ] . IEEE Communications Magazine , 2025 .

Zhang J F , Guo S Y , Gong S Q , et al . Intelligent waveform design for integrated sensing and communication [J ] . IEEE Wireless Communications , 2024 , 32 ( 1 ): 166 - 173 .

Jiang P , Li M , Liu R , et al . SLP-based dual-functional waveform design for ISAC systems: A deep learning approach [J ] . IEEE Transactions on Vehicular Technology , 2025 .

Zhou Y , An Q C , Wang Z B , et al . Integrated Sensing, Computation, and Communication Enabled Federated Edge Learning [J ] . IEEE Transactions on Wireless Communications , 2025 , 25 : 7117 - 7131 .

Zhang J P , Yan S , Peng M G , et al . Coordinated multi-point enabled ISAC under asynchronous errors: Performance analysis and waveform-beamforming optimization [J ] . IEEE Transactions on Vehicular Technology , 2025 .

Zhang J X , Xu S , Li C G , et al . Efficient beam selection for ISAC in cell-free massive MIMO via digital twin-assisted deep reinforcement learning [J ] . IEEE Transactions on Wireless Communications , 2026 , 25 : 9875 - 9890 .

Qi Q , Chen X M , Khalili A , et al . Integrating sensing, computing, and communication in 6G wireless networks: Design and optimization [J ] . IEEE Transactions on Communications , 2022 , 70 ( 9 ): 6212 - 6227 .

Wei Y M , Peng M G , Liu Y Q . Intent-based networks for 6G: Insights and challenges [J ] . Digital Communications and Networks , 2020 , 6 ( 3 ): 270 - 280 .

Liu B H , Peng M G , Li J J , et al . Integrated Communication and Navigation Enabled Low Earth Orbit Satellite Systems [J ] . IEEE Network , 2025 .

Zhang S H , Yan S , Tang Z L , et al . Joint Multi-Service Resource Optimization for Integrated Sensing, Communication, and Computing Networks [J ] . IEEE Internet of Things Journal , 2025 .

Ren Y J , Sun Y H , Peng M G . Deep reinforcement learning based computation offloading in fog enabled industrial internet of things [J ] . IEEE Transactions on Industrial Informatics , 2020 , 17 ( 7 ): 4978 - 4987 .

Sun Y H , Peng M G , Mao S W . Deep reinforcement learning-based mode selection and resource management for green fog radio access networks [J ] . IEEE Internet of Things Journal , 2018 , 6 ( 2 ): 1960 - 1971 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Advances and challenges of AI-driven wireless indoor positioning

AI agent communication network (ACN): a new network paradigm for 6G

6G knowledge system construction: academic knowledge mining and on-demand application for full domains and omni scenarios

Deep image semantic communication model for 6G

Research and optimization on the sensing algorithm for 6G integrated sensing and communication network

Related Author

Wang Huiqiang

Gao Kaixuan

Lyu Hongwu

Feng Guangsheng

Huang Xinling

SUN Tao

DUAN Xiaodong

HUANG Zhenglei

Related Institution

College of Computer Science and Technology, Harbin Engineering University

China Mobile Research Institute

State Key Laboratory of Integrated Services Network, Xidian University

School of Information Science and Engineering, Hunan Normal University

Changsha Social Laboratory of Artificial Intelligence, Hunan University of Technology and Business

AI问答

⁰