CLC number: TN929.5
On-line Access: 2025-03-07
Received: 2024-07-05
Revision Accepted: 2024-10-28
Crosschecked: 2025-03-07
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Guangyi LIU, Juan DENG, Yanhong ZHU, Na LI, Boxiao HAN, Shoufeng WANG, Hua RUI, Jingyu WANG, Jianhua ZHANG, Ying CUI, Yingping CUI, Yang YANG, Yan ZHANG, Jiangzhou WANG, Ye OUYANG, Xiaozhou YE, Tao CHEN, Rongpeng LI, Yongdong ZHU, Yuanyuan ZHANG, Li YANG, Sen BIAN, Wanfei SUN, Qingbi ZHENG, Zhou TONG, Huimin ZHANG, Zecai SHAO, Jiajun WU, Mancong KANG. 6G autonomous radio access network empowered by artificial intelligence and network digital twin[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(2): 161-213.
@article{title="6G autonomous radio access network empowered by artificial intelligence and network digital twin",
author="Guangyi LIU, Juan DENG, Yanhong ZHU, Na LI, Boxiao HAN, Shoufeng WANG, Hua RUI, Jingyu WANG, Jianhua ZHANG, Ying CUI, Yingping CUI, Yang YANG, Yan ZHANG, Jiangzhou WANG, Ye OUYANG, Xiaozhou YE, Tao CHEN, Rongpeng LI, Yongdong ZHU, Yuanyuan ZHANG, Li YANG, Sen BIAN, Wanfei SUN, Qingbi ZHENG, Zhou TONG, Huimin ZHANG, Zecai SHAO, Jiajun WU, Mancong KANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="26",
number="2",
pages="161-213",
year="2025",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400569"
}
%0 Journal Article
%T 6G autonomous radio access network empowered by artificial intelligence and network digital twin
%A Guangyi LIU
%A Juan DENG
%A Yanhong ZHU
%A Na LI
%A Boxiao HAN
%A Shoufeng WANG
%A Hua RUI
%A Jingyu WANG
%A Jianhua ZHANG
%A Ying CUI
%A Yingping CUI
%A Yang YANG
%A Yan ZHANG
%A Jiangzhou WANG
%A Ye OUYANG
%A Xiaozhou YE
%A Tao CHEN
%A Rongpeng LI
%A Yongdong ZHU
%A Yuanyuan ZHANG
%A Li YANG
%A Sen BIAN
%A Wanfei SUN
%A Qingbi ZHENG
%A Zhou TONG
%A Huimin ZHANG
%A Zecai SHAO
%A Jiajun WU
%A Mancong KANG
%J Frontiers of Information Technology & Electronic Engineering
%V 26
%N 2
%P 161-213
%@ 2095-9184
%D 2025
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400569
TY - JOUR
T1 - 6G autonomous radio access network empowered by artificial intelligence and network digital twin
A1 - Guangyi LIU
A1 - Juan DENG
A1 - Yanhong ZHU
A1 - Na LI
A1 - Boxiao HAN
A1 - Shoufeng WANG
A1 - Hua RUI
A1 - Jingyu WANG
A1 - Jianhua ZHANG
A1 - Ying CUI
A1 - Yingping CUI
A1 - Yang YANG
A1 - Yan ZHANG
A1 - Jiangzhou WANG
A1 - Ye OUYANG
A1 - Xiaozhou YE
A1 - Tao CHEN
A1 - Rongpeng LI
A1 - Yongdong ZHU
A1 - Yuanyuan ZHANG
A1 - Li YANG
A1 - Sen BIAN
A1 - Wanfei SUN
A1 - Qingbi ZHENG
A1 - Zhou TONG
A1 - Huimin ZHANG
A1 - Zecai SHAO
A1 - Jiajun WU
A1 - Mancong KANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 26
IS - 2
SP - 161
EP - 213
%@ 2095-9184
Y1 - 2025
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2400569
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.
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