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6G autonomous radio access network empowered by artificial intelligence and network digital twin

人工智能和数字孪生使能的6G自主无线接入网

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  • Published: 28 December 2024
  • Volume 26, pages 161–213, (2025)
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6G autonomous radio access network empowered by artificial intelligence and network digital twin
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  • Guangyi Liu  (刘光毅)  ORCID: orcid.org/0000-0002-8656-19461,2,
  • Juan Deng  (邓娟)1,2,
  • Yanhong Zhu  (朱艳宏)1,
  • Na Li  (李娜)1,2,
  • Boxiao Han  (韩伯骁)2,
  • Shoufeng Wang  (王首峰)3,
  • Hua Rui  (芮华)4,
  • Jingyu Wang  (王敬宇)5,
  • Jianhua Zhang  (张建华)5,
  • Ying Cui  (崔颖)6,
  • Yingping Cui  (崔莹萍)1,
  • Yang Yang  (杨旸)6,
  • Yan Zhang  (张彦)7,
  • Jiangzhou Wang  (王江舟)8,
  • Ye Ouyang  (欧阳晔)3,
  • Xiaozhou Ye  (叶晓舟)3,
  • Tao Chen  (陈滔)9,
  • Rongpeng Li  (李荣鹏)10,
  • Yongdong Zhu  (朱永东)11,
  • Yuanyuan Zhang  (张园园)9,
  • Li Yang  (杨立)4,
  • Sen Bian  (边森)3,
  • Wanfei Sun  (孙万飞)12,
  • Qingbi Zheng  (郑青碧)1,
  • Zhou Tong  (佟舟)1,
  • Huimin Zhang  (张慧敏)1,
  • Zecai Shao  (邵泽才)1,
  • Jiajun Wu  (吴佳骏)1 &
  • …
  • Mancong Kang  (康曼聪)1 

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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。

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Authors and Affiliations

  1. China Mobile Research Institute, Beijing, 100053, China

    Guangyi Liu  (刘光毅), Juan Deng  (邓娟), Yanhong Zhu  (朱艳宏), Na Li  (李娜), Yingping Cui  (崔莹萍), Qingbi Zheng  (郑青碧), Zhou Tong  (佟舟), Huimin Zhang  (张慧敏), Zecai Shao  (邵泽才), Jiajun Wu  (吴佳骏) & Mancong Kang  (康曼聪)

  2. ZGC Institute of Ubiquitous-X Innovation and Applications, Beijing, 100080, China

    Guangyi Liu  (刘光毅), Juan Deng  (邓娟), Na Li  (李娜) & Boxiao Han  (韩伯骁)

  3. AsiaInfo Technologies (China) Inc., Beijing, 100193, China

    Shoufeng Wang  (王首峰), Ye Ouyang  (欧阳晔), Xiaozhou Ye  (叶晓舟) & Sen Bian  (边森)

  4. ZTE Corporation, Shenzhen, 518057, China

    Hua Rui  (芮华) & Li Yang  (杨立)

  5. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Jingyu Wang  (王敬宇) & Jianhua Zhang  (张建华)

  6. IoT Thrust and Research Center for Digital World with Intelligent Things, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 511455, China

    Ying Cui  (崔颖) & Yang Yang  (杨旸)

  7. Department of Informatics, University of Oslo, Oslo, 0316, Norway

    Yan Zhang  (张彦)

  8. School of Engineering and Digital Arts, University of Kent, Canterbury, CT2 7NZ, UK

    Jiangzhou Wang  (王江舟)

  9. MediaTek (Beijing) Inc., Beijing, 100015, China

    Tao Chen  (陈滔) & Yuanyuan Zhang  (张园园)

  10. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310058, China

    Rongpeng Li  (李荣鹏)

  11. Zhejiang Lab, Hangzhou, 311500, China

    Yongdong Zhu  (朱永东)

  12. CICT Mobile Communication Technology Co., Ltd., Beijing, 100083, China

    Wanfei Sun  (孙万飞)

Authors
  1. Guangyi Liu  (刘光毅)
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  25. Zhou Tong  (佟舟)
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  26. Huimin Zhang  (张慧敏)
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  27. Zecai Shao  (邵泽才)
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  28. Jiajun Wu  (吴佳骏)
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  29. Mancong Kang  (康曼聪)
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Contributions

Guangyi LIU designed the research. All the authors drafted and revised the paper.

Corresponding author

Correspondence to Guangyi Liu  (刘光毅).

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)

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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

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  • Received: 05 July 2024

  • Accepted: 28 October 2024

  • Published: 28 December 2024

  • Issue Date: February 2025

  • DOI: https://doi.org/10.1631/FITEE.2400569

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Key words

  • 6G
  • Network autonomy
  • Native artificial intelligence
  • Network digital twin
  • Service-based radio access network

关键词

  • 6G
  • 网络自治
  • 内生人工智能
  • 网络数字孪生
  • 服务化无线接入网

CLC number

  • TN929.5
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