Abstract
The sixth-generation (6G) mobile network implements the social vision of digital twins and ubiquitous intelligence. Contrary to the fifth-generation (5G) mobile network that focuses only on communications, 6G mobile networks must natively support new capabilities such as sensing, computing, artificial intelligence (AI), big data, and security while facilitating Everything as a Service. Although 5G mobile network deployment has demonstrated that network automation and intelligence can simplify network operation and maintenance (O&M), the addition of external functionalities has resulted in low service efficiency and high operational costs. In this study, a technology framework for a 6G autonomous radio access network (RAN) is proposed to achieve a high-level network autonomy that embraces the design of native cloud, native AI, and network digital twin (NDT). First, a service-based architecture is proposed to re-architect the protocol stack of RAN, which flexibly orchestrates the services and functions on demand as well as customizes them into cloud-native services. Second, a native AI framework is structured to provide AI support for the diverse use cases of network O&M by orchestrating communications, AI models, data, and computing power demanded by AI use cases. Third, a digital twin network is developed as a virtual environment for the training, pre-validation, and tuning of AI algorithms and neural networks, avoiding possible unexpected losses of the network O&M caused by AI applications. The combination of native AI and NDT can facilitate network autonomy by building closed-loop management and optimization for RAN.
摘要
第六代(6G)移动网络将实现数字孪生与泛在智能的社会愿景。与仅专注于通信的第五代(5G)移动网络不同，6G移动网络需要内生支持诸如感知、计算、人工智能(Artificial Intelligence, AI)、大数据和安全等新功能，同时推动一切即服务(Everything as a Service, XaaS)的实现。尽管5G移动网络的部署已经证明网络自动化和智能化能够简化网络运维(Operation and Maintenance, O&M)的流程，但外部功能的增加却导致了服务效率低下和运维成本上升。因此，本研究提出一种6G自治无线接入网(Radio Access Network, RAN)的技术框架，旨在实现高水平的网络自治；该框架融合了云原生、内生AI和网络数字孪生(Network Digital Twin, NDT)的设计理念。首先，我们提出了服务化的架构，用于重新构建RAN的协议栈。这一架构能够按需灵活编排服务和功能，并将其定制为云原生服务。其次，我们构建了内生AI框架，通过编排AI用例所需的通信、AI模型、数据和计算能力，为网络运维的多样化用例提供AI支持。第三，我们引入了数字孪生网络，作为AI算法和神经网络的训练、预验证和调优的虚拟环境。这一环境能够避免AI应用可能给网络运维带来的风险。通过内生AI与NDT的结合，可以构建RAN的闭环管理和优化，进一步促进网络自治的实现。
Article PDF
Similar content being viewed by others
References
3GPP, 2017. Study on New Radio Access Technology: Radio Access Architecture and Interfaces. TR 38.801, France.
3GPP, 2023a. Evolved Universal Terrestrial Radio Access (EUTRA) and NR; Service Data Adaptation Protocol (SDAP) Specification. TS 37.324, France.
3GPP, 2023b. Management and Orchestration; Levels of Autonomous Network. TS 28.100, France.
3GPP, 2023c. NR; Medium Access Control (MAC) Protocol Specification. TS 38.321, France.
3GPP, 2023d. NR; Packet Data Convergence Protocol (PDCP) Specification. TS 38.323, France.
3GPP, 2023e. NR; Radio Link Control (RLC) Protocol Specification. TS 38.322, France.
3GPP, 2023f. NR; Services Provided by the Physical Layer. TS 38.202, France.
Abdullah M, Madain A, Jararweh Y, 2022. ChatGPT: fundamentals, applications and social impacts. 9th Int Conf on Social Networks Analysis, Management and Security, p.1–8. https://doi.org/10.1109/SNAMS58071.2022.10062688
Adem N, Benfaid A, Harib R, et al., 2021. How crucial is it for 6G networks to be autonomous? https://doi.org/10.48550/arXiv.2106.06949
Almasan P, Ferriol-Galmés M, Paillisse J, et al., 2022. Network digital twin: context, enabling technologies, and opportunities. IEEE Commun Mag, 60(11): 22–27. https://doi.org/10.1109/MCOM.001.2200012
Banerjee A, Mwanje SS, Carle G, 2021. An intent-driven orchestration of cognitive autonomous networks for RAN management. 17th Int Conf on Network and Service Management, p.380–384. https://doi.org/10.23919/CNSM52442.2021.9615505
Benzaid C, Taleb T, 2020. AI-driven zero touch network and service management in 5G and beyond: challenges and research directions. IEEE Netw, 34(2): 186–194. https://doi.org/10.1109/MNET.001.1900252
Bhat JR, Alqahtani SA, 2021. 6G ecosystem: current status and future perspective. IEEE Access, 9:43134–43167. https://doi.org/10.1109/ACCESS.2021.3054833
Bonati L, 2022. Softwarized Approaches for the Open RAN of NextG Cellular Networks. PhD Dissemination, Northeastern University, Boston, USA.
Boutaba R, Shahriar N, Salahuddin MA, et al., 2021. AI-driven closed-loop automation in 5G and beyond mobile networks. Proc 4th FlexNets Workshop on Flexible Networks Artificial Intelligence Supported Network Flexibility and Agility, p.1–6. https://doi.org/10.1145/3472735.3474458
Cha J, Moon Y, Cho S, et al., 2022. RAN-CN converged userplane for 6G cellular networks. IEEE Global Communications Conf, p.2843–2848. https://doi.org/10.1109/GLOBECOM48099.2022.10001487
Chen YX, Li RP, Zhao ZF, et al., 2024. NetGPT: an AI-native network architecture for provisioning beyond personalized generative services. IEEE Netw, 38(6): 404–413. https://doi.org/10.1109/MNET.2024.3376419
China Mobile, 2021. China Mobile Network Autonomous Driving White Paper (in Chinese). extension://bfdogplmndidlpjfhoijckpakkdjkkil/pdf/viewer.html?file=https%3A%2F%2Flinproxy.fan.workers.dev%3A443%2Fhttps%2Fkxlabs.10086.cn%2Ffiles%2F1626350861865-520854.pdf [Accessed on July 26, 2024].
China Mobile, 2022. 6G Service-Based RAN White Paper (in Chinese). extension://bfdogplmndidlpjfhoijckpakkdjkkil/pdf/viewer.html?file=https%3A%2F%2Flinproxy.fan.workers.dev%3A443%2Fhttps%2F13115299.s21i.faiusr.com%2F61%2F1%2FABUIABA9GAAg_smAkQYooOzG3wQ.pdf [Accessed on Aug. 1, 2024].
China Mobile, 2023. 6G Service-Based RAN White Paper (in Chinese). extension://bfdogplmndidlpjfhoijckpakkdjkkil/pdf/viewer.html?file=https%3A%2F%2Flinproxy.fan.workers.dev%3A443%2Fhttps%2F13115299.s21i.faiusr.com%2F61%2F1%2FABUIABA9GAAg-be-qQYoivyeKA.pdf [Accessed on July 28, 2024].
Choi J, Sharma N, Gantha SS, et al., 2022. RAN-CN converged control-plane for 6G cellular networks. IEEE Global Communications Conf, p.1253–1258. https://doi.org/10.1109/GLOBECOM48099.2022.10001281
Coronado E, Behravesh R, Subramanya T, et al., 2022. Zero touch management: a survey of network automation solutions for 5G and 6G networks. IEEE Commun Surv Tut, 24(4): 2535–2578. https://doi.org/10.1109/COMST.2022.3212586
Cui YP, Lv TJ, Ni W, et al., 2023. Digital twin-aided learning for managing reconfigurable intelligent surface-assisted, uplink, user-centric cell-free systems. IEEE J Sel Areas Commun, 41(10): 3175–3190. https://doi.org/10.1109/JSAC.2023.3310050
DeAlmeida JM, Pontes CFT, Dasilva LA, et al., 2021. Abnormal behavior detection based on traffic pattern categorization in mobile networks. IEEE Trans Netw Serv Manag, 18(4): 4213–4224. https://doi.org/10.1109/TNSM.2021.3125019
Deng J, Tian KC, Zheng QB, et al., 2022. Cloud-assisted distributed edge brains for multi-cell joint beamforming optimization for 6G. China Commun, 19(3): 36–49. https://doi.org/10.23919/JCC.2022.03.003
Duan XY, Kang HH, Zhang JJ, 2022. Autonomous network technology innovation in digital and intelligent era. ZTE Commun, 20(4): 52–61. https://doi.org/10.12142/ZTECOM.202204007
Eriksson D, Pearce M, Gardner JR, et al., 2019. Scalable global optimization via local Bayesian optimization. Proc 33rd Conf on Neural Information Processing Systems, p.5496–5507.
Ferriol-Galmés M, Suárez-Varela J, Paillissé J, et al., 2022. Building a digital twin for network optimization using graph neural networks. Comput Netw, 217:109329. https://doi.org/10.1016/j.comnet.2022.109329
Gill SS, Xu MX, Ottaviani C, et al., 2022. AI for next generation computing: emerging trends and future directions. Int Things, 19:100514. https://doi.org/10.1016/j.iot.2022.100514
Hazra A, Morichetta A, Murturi I, et al., 2024. Distributed AI in zero-touch provisioning for edge networks: challenges and research directions. Computer, 57(3): 69–78. https://doi.org/10.1109/MC.2023.3334913
He WL, Zhang C, Deng J, et al., 2023. Conditional generative adversarial network aided digital twin network modeling for massive MIMO optimization. IEEE Wireless Communications and Networking Conf, p.1–5. https://doi.org/10.1109/WCNC55385.2023.10118756
He XW, Yang ZM, Xiang Y, et al., 2023. NWDAF in 3GPP 5G advanced: a survey. 3rd Int Conf on Electronic Information Engineering and Computer Science, p.756–761. https://doi.org/10.1109/EIECS59936.2023.10435433
Hu F, Hao Q, Bao K, 2014. A survey on software-defined network and OpenFlow: from concept to implementation. IEEE Commun Surv Tut, 16(4): 2181–2206. https://doi.org/10.1109/COMST.2014.2326417
Huawei, 2023. Autonomous Driving Network (ADN). https://carrier.huawei.com/en/adn [Accessed on July 23, 2024].
Hui SD, Wang HD, Li T, et al., 2023. Large-scale urban cellular traffic generation via knowledge-enhanced GANs with multi-periodic patterns. Proc 29th ACM SIGKDD Conf on Knowledge Discovery and Data Mining, p.4195–4206. https://doi.org/10.1145/3580305.3599853
Institute CMCCR, 2022. 6G Autonomous Mobile Network Enabled by Digital Twin Network White Paper (in Chinese). https://www.sgpjbg.com/baogao/64570.html [Accessed on July 30, 2024].
Ismail T, Mahmoud HHM, 2020. Optimum functional splits for optimizing energy consumption in V-RAN. IEEE Access, 8:194333–194341. https://doi.org/10.1109/ACCESS.2020.3033879
ITU-R, 2023. Framework and Overall Objectives of the Future Development of IMT for 2030 and Beyond. https://techblog.comsoc.org/2023/01/29/ [Accessed on Aug. 12, 2024].
Jain R, Paul S, 2013. Network virtualization and software defined networking for cloud computing: a survey. IEEE Commun Mag, 51(11): 24–31. https://doi.org/10.1109/MCOM.2013.6658648
Jiang L, Wang XS, Yang AD, et al., 2023. An efficient multiagent optimization approach for coordinated massive MIMO beamforming. IEEE Int Conf on Communications, p.5632–5638. https://doi.org/10.1109/ICC45041.2023.10279724
Jiang W, Han B, Habibi MA, et al., 2021. The road towards 6G: a comprehensive survey. IEEE Open J Commun Soc, 2:334–366. https://doi.org/10.1109/OJCOMS.2021.3057679
Kalogiros C, Muschamp P, Caruso G, et al., 2021. Capabilities of business and operational support systems for precommercial 5G testbeds. IEEE Commun Mag, 59(12): 58–64. https://doi.org/10.1109/MCOM.003.2001059
Kamran R, Kiran S, Jha P, et al., 2024. Green 6G: energy awareness in design. 16th Int Conf on Communication Systems & Networks, p.1122–1125. https://doi.org/10.1109/COMSNETS59351.2024.10427334
Kaur J, Khan MA, 2022. Sixth generation (6G) wireless technology: an overview, vision, challenges and use cases. IEEE Region 10 Symp, p.1–6. https://doi.org/10.1109/TENSYMP54529.2022.9864388
Khan TA, Abbas K, Muhammad A, et al., 2022. An intentdriven closed-loop platform for 5G network service orchestration. Comput Mater Con, 70(3): 4323–4340. https://doi.org/10.32604/cmc.2022.017118
Kim H, Feamster N, 2013. Improving network management with software defined networking. IEEE Commun Mag, 51(2): 114–119. https://doi.org/10.1109/MCOM.2013.6461195
Lähdekorpi P, Hronec M, Jolma P, et al., 2017. Energy efficiency of 5G mobile networks with base station sleep modes. IEEE Conf on Standards for Communications and Networking, p.163–168. https://doi.org/10.1109/CSCN.2017.8088616
Li LL, 2024. A survey on intelligence-endogenous network: architecture and technologies for future 6G. Intell Conv Netw, 5(1): 53–67. https://doi.org/10.23919/ICN.2024.0005
Li N, Liu GY, Zhang HM, et al., 2022a. Micro-service-based radio access network. China Commun, 19(3): 1–15. https://doi.org/10.23919/JCC.2022.03.001
Li N, Liu GY, Zhang HM, et al., 2022b. Service-based RAN: the next phase of cloud RAN. IEEE Globecom Workshops, p.1206–1211. https://doi.org/10.1109/GCWkshps56602.2022.10008666
Li Q, Ding ZR, Tong XP, et al., 2022. 6G cloud-native system: vision, challenges, architecture framework and enabling technologies. IEEE Access, 10:96602–96625. https://doi.org/10.1109/ACCESS.2022.3205341
Liu GY, Jin J, Wang QX, 2020a. Vision and requirements of 6G: digital twin and ubiquitous intelligence. Mob Commun, 44(6): 3–9 (in Chinese). https://doi.org/10.3969/j.issn.1006-1010.2020.06.001
Liu GY, Huang YH, Li N, et al., 2020b. Vision, requirements and network architecture of 6G mobile network beyond 2030. China Commun, 17(9): 92–104. https://doi.org/10.23919/JCC.2020.09.008
Liu GY, Li N, Deng J, et al., 2022. The SOLIDS 6G mobile network architecture: driving forces, features, and functional topology. Engineering, 8:42–59. https://doi.org/10.1016/j.eng.2021.07.013
Liu GY, Zhang HM, Tong Z, et al., 2024. 6G mobile information network architecture: migrate from communication to XaaS. Sci Sin Inform, 54(5): 1236–1266 (in Chinese). https://doi.org/10.1360/SSI-2023-0339
Liu ZH, Zhang M, Zhang CH, et al., 2023. 6G network selfevolution: generating core networks. IEEE Int Conf on Communications Workshops, p.625–630. https://doi.org/10.1109/ICCWorkshops57953.2023.10283790
Long QY, Chen YL, Zhang HJ, et al., 2022. Software defined 5G and 6G networks: a survey. Mob Netw Appl, 27(5): 1792–1812. https://doi.org/10.1007/s11036-019-01397-2
Lu YL, Maharjan S, Zhang Y, 2021. Adaptive edge association for wireless digital twin networks in 6G. IEEE Int Things J, 8(22): 16219–16230. https://doi.org/10.1109/JIOT.2021.3098508
Maharana K, Mondal S, Nemade B, 2022. A review: data preprocessing and data augmentation techniques. Glob Trans Proc, 3(1): 91–99. https://doi.org/10.1016/j.gltp.2022.04.020
Mahbub M, Shubair RM, 2022. Energy efficient maximization of user association employing IRS in mmWave multi-tier 6G networks. IEEE Int Conf on Sensing, Communication, and Networking, p.25–30. https://doi.org/10.1109/SECONWorkshops56311.2022.9926334
Mai VS, La RJ, Zhang T, et al., 2022. End-to-end quality-ofservice assurance with autonomous systems: 5G/6G case study. IEEE 19th Annual Consumer Communications & Networking Conf, p.644–651. https://doi.org/10.1109/CCNC49033.2022.9700514
Mao BM, Tang FX, Kawamoto Y, et al., 2022. AI models for green communications towards 6G. IEEE Commun Surv Tut, 24(1): 210–247. https://doi.org/10.1109/COMST.2021.3130901
Mehmood K, Kralevska K, Palma D, 2023. Intent-driven autonomous network and service management in future cellular networks: a structured literature review. Comput Netw, 220:109477. https://doi.org/10.1016/j.comnet.2022.109477
Nidhi, Mihovska A, Kumar A, et al., 2022. Business opportunities for beyond 5G and 6G networks. 25th Int Symp on Wireless Personal Multimedia Communications, p.543–548. https://doi.org/10.1109/WPMC55625.2022.10014752
Niemöller J, Müller E, Maggiari M, et al., 2024. Evolving service management towards intent-driven autonomous networks. Ericss Technol Rev, 2024(3): 2–7.
Niknam S, Dhillon HS, Reed JH, 2020. Federated learning for wireless communications: motivation, opportunities, and challenges. IEEE Commun Mag, 58(6): 46–51. https://doi.org/10.1109/MCOM.001.1900461
Patwardhan N, Marrone S, Sansone C, 2023. Transformers in the real world: a survey on NLP applications. Information, 14(4): 242. https://doi.org/10.3390/info14040242
Pivoto DGS, Rezende TT, Facina MSP, et al., 2023. A detailed relevance analysis of enabling technologies for 6G architectures. IEEE Access, 11:89644–89684. https://doi.org/10.1109/ACCESS.2023.3301811
Qin Z, Deng SG, Yan XQ, et al., 2023. 6G data plane: a novel architecture enabling data collaboration with arbitrary topology. Mob Netw Appl, 28(1): 394–405. https://doi.org/10.1007/s11036-023-02093-y
Raj DRR, Shaik TA, Hirwe A, et al., 2023. Building a digital twin network of SDN using knowledge graphs. IEEE Access, 11:63092–63106. https://doi.org/10.1109/ACCESS.2023.3288813
Rohani R, 2023. Function vs Service vs Platform. https://rlohani.medium.com/function-vs-service-vs-platforme2ac25445167 [Accessed on July 29, 2024].
Shahjalal M, Kim W, Khalid W, et al., 2023. Enabling technologies for AI empowered 6G massive radio access networks. ICT Exp, 9(3): 341–355. https://doi.org/10.1016/j.icte.2022.07.002
Sun YT, Zhang JH, Yu L, et al., 2023. How to define the propagation environment semantics and its application in scatterer-based beam prediction. IEEE Wirel Commun Lett, 12(4): 649–653. https://doi.org/10.1109/LWC.2023.3237827
Tang QQ, Xie RC, Fang ZR, et al., 2024a. Joint service deployment and task scheduling for satellite edge computing: a two-timescale hierarchical approach. IEEE J Sel Areas Commun, 42(5): 1063–1079. https://doi.org/10.1109/JSAC.2024.3365889
Tang QQ, Xie RC, Feng L, et al., 2024b. SIaTS: a service intent-aware task scheduling framework for computing power networks. IEEE Netw, 38(4): 233–240. https://doi.org/10.1109/MNET.2023.3326239
Tao ZY, Xu W, You XH, 2023. Digital twin assisted deep reinforcement learning for online admission control in sliced network. https://doi.org/10.48550/arXiv.2310.09299
TG3, 2023. Wireless Network Data Dictionary White Paper (in Chinese). https://www.6g-ana.com/upload/file/20231214/6383817255076725588362734.pdf [Accessed on Aug. 16, 2024].
Umoga UJ, Sodiya EO, Ugwuanyi ED, et al., 2024. Exploring the potential of AI-driven optimization in enhancing network performance and efficiency. Magna Sci Adv Res Rev, 10(1): 368–378. https://doi.org/10.30574/msarr.2024.10.1.0028
Villalobos P, Ho A, Sevilla J, et al., 2024. Will we run out of data? Limits of LLM scaling based on human-generated data. https://doi.org/10.48550/arXiv.2211.04325
Wang S, Sun T, Yang HW, et al., 2020. 6G network: towards a distributed and autonomous system. 2nd 6G Wireless Summit, p.1–5. https://doi.org/10.1109/6GSUMMIT49458.2020.9083888
Wang SF, Chen HM, Ouyang Y, et al., 2023a. Digital twin network application requirement on green coordination of computing and networking. IEEE 3rd Int Conf on Digital Twins and Parallel Intelligence, p.1–6. https://doi.org/10.1109/DTPI59677.2023.10365446
Wang SF, Chen HM, Ouyang Y, et al., 2023b. Elastic digital twin network modeling fulfilling twining dynamic in network life cycle. IEEE 3rd Int Conf on Digital Twins and Parallel Intelligence, p.1–7. https://doi.org/10.1109/DTPI59677.2023.10365450
Wu JJ, Li RP, An XL, et al., 2021. Toward native artificial intelligence in 6G networks: system design, architectures, and paradigms. https://doi.org/10.48550/arXiv.2103.02823
Yan XQ, An XL, Yu WX, et al., 2021. A blockchain-based subscriber data management scheme for 6G mobile communication system. IEEE Globecom Workshop, p.1–6. https://doi.org/10.1109/GCWkshps52748.2021.9682154
Yang CG, Mi XR, Ouyang Y, et al., 2023. Smart intent-driven network management. IEEE Commun Mag, 61(1): 106–112. https://doi.org/10.1109/MCOM.002.2200119
Yang Y, Ma ML, Wu HQ, et al., 2023. 6G network AI architecture for everyone-centric customized services. IEEE Netw, 37(5): 71–80. https://doi.org/10.1109/MNET.124.2200241
Yang YQ, Yang SS, Zhao C, et al., 2024. TelOps: AI-driven operations and maintenance for telecommunication networks. IEEE Commun Mag, 62(4): 104–110. https://doi.org/10.1109/MCOM.003.2300055
Yaqoob M, Trestian R, Tatipamula M, et al., 2024. Digitaltwin- driven end-to-end network slicing toward 6G. IEEE Int Comput, 28(2): 47–55. https://doi.org/10.1109/MIC.2023.3332252
Younes M, Louet Y, 2022. Joint optimization of energy consumption and spectral efficiency for 5G/6G point-to-point networks. 3rd URSI Atlantic and Asia Pacific Radio Science Meeting, p.1–4. https://doi.org/10.23919/AT-AP-RASC54737.2022.9814348
Yu L, Zhang YX, Zhang JH, et al., 2022. Implementation framework and validation of cluster-nuclei based channel model using environmental mapping for 6G communication systems. China Commun, 19(4): 1–13. https://doi.org/10.23919/JCC.2022.04.001
Zhang D, Zhao YJ, Zhao ZC, et al., 2024. Research on intelligent operation architecture and evolution of 6G network. Des Technol Post Telecommun, 2024(3): 32–37 (in Chinese). https://doi.org/10.12045/j.issn.1007-3043.2024.03.007
Zhang LF, Hu ZY, Li YZ, et al., 2022. Architecture and applications of wireless autonomous network. IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles, p.2048–2051. https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00296
Zhang SY, Li T, Hui SD, et al., 2023. Deep transfer learning for city-scale cellular traffic generation through urban knowledge graph. Proc 29th ACM SIGKDD Conf on Knowledge Discovery and Data Mining, p.4842–4851. https://doi.org/10.1145/3580305.3599801
Zhao BR, Cui QM, Liang SY, et al., 2022. Green concerns in federated learning over 6G. China Commun, 19(3): 50–69. https://doi.org/10.23919/JCC.2022.03.004
Zhu YH, Chen DY, Zhou C, et al., 2021. A knowledge graph based construction method for digital twin network. IEEE 1st Int Conf on Digital Twins and Parallel Intelligence, p.362–365. https://doi.org/10.1109/DTPI52967.2021.9540177
Ziegler V, Viswanathan H, Flinck H, et al., 2020. 6G architecture to connect the worlds. IEEE Access, 8:173508–173520. https://doi.org/10.1109/ACCESS.2020.3025032
Zong JY, Liu HT, Liu Y, et al., 2022. Service-based architecture evolution of radio access network towards 6G. Proc 12th Int Conf on Computer Engineering and Networks, p.525–534. https://doi.org/10.1007/978-981-19-6901-0_56
Author information
Authors and Affiliations
Contributions
Guangyi LIU designed the research. All the authors drafted and revised the paper.
Corresponding author
Ethics declarations
Guangyi LIU is the executive lead editor of this special issue; Jianhua ZHANG, Yang YANG, Yan ZHANG, and Jiangzhou WANG are guest editors of this special issue. Jianhua ZHANG is also an executive associate editor-in-chief of Frontiers of Information Technology & Electronic Engineering. They were not involved with the peer review process of this paper. All the authors declare that they have no conflict of interest.
Additional information
Project supported by the National Key Research and Development Program of China (No. 2024YFE0200600)
Rights and permissions
Open access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third-party materials in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://doi.org/creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Liu, G., Deng, J., Zhu, Y. et al. 6G autonomous radio access network empowered by artificial intelligence and network digital twin. Front Inform Technol Electron Eng 26, 161–213 (2025). https://doi.org/10.1631/FITEE.2400569
Received:
Accepted:
Published:
Version of record:
Issue date:
DOI: https://doi.org/10.1631/FITEE.2400569
Key words
- 6G
- Network autonomy
- Native artificial intelligence
- Network digital twin
- Service-based radio access network