SHI Yuanyuan,WEI Bin,LI Aihua,et al.Research on the evolution, key technologies, and applications for AI enabled 5G-Advanced core network running[J].Telecommunications Science,2024,40(12):146-162.
SHI Yuanyuan,WEI Bin,LI Aihua,et al.Research on the evolution, key technologies, and applications for AI enabled 5G-Advanced core network running[J].Telecommunications Science,2024,40(12):146-162. DOI: 10.11959/j.issn.1000-0801.2024255.
Research on the evolution, key technologies, and applications for AI enabled 5G-Advanced core network running
5G serves as the pivotal infrastructure for the emerging era of the Internet of everything
with intelligentization being its crucial direction. In the 5G-Advanced (5G-A) phase
AI enabled core network running can substantially enhance network efficiency
facilitate real-time perception
and precisely cater to diversified service demands through the deep integration of AI technology and network running mechanisms. For the 5G-A network
the overall intelligent architecture
key technologies
and applications of AI enabled core network operations were highlighted. Firstly
the development trend of AI enabled 5G-A core network running was analyzed and key intelligent applications were elaborated. Secondly
AI enabled core network running architecture combining four layers and four dimensions was proposed
encompassing a central intelligent analysis layer
a control plane intelligent endogenous layer
an edge real-time inference layer
and an on-device lightweight inference layer. This architecture enables customized
real-time data collection across four dimensions: slices
network elements
users
and service flows
thereby offering intelligent analysis services such as data perception
training
and inference. Then
five key technologies and capabilities of AI enabled core network running were introduced. Finally
focusing on the typical service requirements of AI enabled core network running
the relevant solutions and their results were analyzed and summarized
thus providing new ideas for the application and implementation of 5G-A core network intelligence.
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references
CHEN Y X , LI R P , ZHAO Z F , et al . NetGPT: an AI-native network architecture for provisioning beyond personalized generative services [J ] . IEEE Network , 2024 ( 99 ): 1 .
3GPP. Architecture enhancements for 5G system to support network data analytics services (Release 15): TS 23.288 [S ] . 2020 .
中国移动通信有限公司研究院 . 网络运行智能架构与场景白皮书 [R ] . 2024 .
China Mobile Research Institute . Network intelligent architecture and running scene, the white paper [R ] . 2024 .
3GPP. Architecture enhancements for 5G system to support network data analytics services (Release 18): TS 23.288 V18.6.0 [S ] . 2024 .
3GPP. System architecture for the 5G system(5GS); Stage2: TS 23.501 V19.0.0 [S ] . 2024 .
REN C , MA R T , MU J . Research on 5G core network architecture enhancements for supporting native data analytics capability [J ] . Designing Techniques of Posts and Telecommunications , 2021 ( 9 ): 940 - 945 .
XU C W , MCAULEY J . A survey on model compression and acceleration for pretrained language models [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2023 , 37 ( 9 ): 10566 - 10575 .
LI A H , WU X B , CHEN C , et al . Big data intelligent analysis technology for 5G network [J ] . Telecommunications Science , 2022 , 38 ( 8 ): 129 - 139 .
QU G Q , CHEN Q Y , WEI W , et al . Mobile edge intelligence for large language models: a contemporary survey [EB ] . 2024 .
CCSA . Mobile Internet network quality evaluation index and evaluation method for public application Cloud Games: T/CCSA 477-2023 [S ] . 2023 .
TAYLOR V F , SPOLAOR R , CONTI M , et al . Robust smartphone app identification via encrypted network traffic analysis [J ] . IEEE Transactions on Information Forensics and Security , 2018 , 13 ( 1 ): 63 - 78 .
MENG X Y , LIN C C , WANG Y Q , et al . NetGPT: generative pretrained transformer for network traffic [J ] . arXiv prepint arXiv: 2304 . 09513 v 1 , 2023 .
DEVLIN J , CHANG M , LEE K , et al . BERT: pre-training of deep bidirectional transformers for language understanding [C ] // Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . Association for Computational Linguistics , 2019 : 4171 - 4186 .
CHEN Z , BADRINARAYANAN V , LEE C Y , et al . GradNorm: gradient normalization for adaptive loss balancing in deep multitask networks [J ] . arXiv prepint arXiv: 1711.02257 , 2017 .