Intelligence Driven Wireless Networks in B5G and 6G Era: A Survey

doi:10.12142/ZTECOM.202403012

ZTE Communications ›› 2024, Vol. 22 ›› Issue (3): 99-105.DOI: 10.12142/ZTECOM.202403012

• Review • Previous Articles Next Articles

Intelligence Driven Wireless Networks in B5G and 6G Era: A Survey

GAO Yin¹^,²(), CHEN Jiajun¹^,², LI Dapeng¹^,²

^1.State Key Laboratory of Mobile Network and Mobile Multimedia Technology, Shenzhen 518055, China
^2.Wireless Product R&D Institute, ZTE Corporation, Shanghai 201203, China

Received:2024-03-11 Online:2024-09-29 Published:2024-09-29
About author:GAO Yin (gao.yin1@zte.com.cn) received her master’s degree in circuit and system from Xidian University, China in 2005. Since 2005, she has been engaged in the study of 4G/5G technology and is currenly a wireless expert and project manager at the research center of ZTE Corporation and the State Key Laboratory of Mobile Network and Mobile Multimedia Technology, China. She has authored or co-authored hundreds of proposals for 3GPP meetings and journal papers in wireless communications and has filed more than 200 patents. She has also worked as 3GPP RAN3 Chair since 2017 and was elected as RAN3 Chair in May 2021.
CHEN Jiajun received his master’s degree in communication engineer from Shanghai University, China in 2019. He is currently a 5G research engineer at the R&D center of ZTE Corporation and the State Key Laboratory of Mobile Network and Mobile Multimedia Technology, China. His research interests include 5G wireless communications and artificial intelligence.
LI Dapeng received his MS degree in computer science from University of Electronic Science and Technology of China in 2003. He is currently a senior researcher at ZTE corporation and mainly focuses on research and implementation of wireless access network systems.
Supported by:
the National Natural Science Foundation of China(62201266);the Natural Science Foundation of Jiangsu Province(BK20210335)

Abstract

Abstract:

As the wireless communication network undergoes continuous expansion, the challenges associated with network management and optimization are becoming increasingly complex. To address these challenges, the emerging artificial intelligence (AI) and machine learning (ML) technologies have been introduced as a powerful solution. They empower wireless networks to operate autonomously, predictively, on-demand, and with smart functionality, offering a promising resolution to intricate optimization problems. This paper aims to delve into the prevalent applications of AI/ML technologies in the optimization of wireless networks. The paper not only provides insights into the current landscape but also outlines our vision for the future and considerations regarding the development of an intelligent 6G network.

Key words: intelligent network, native AI, load prediction, trajectory prediction

GAO Yin, CHEN Jiajun, LI Dapeng. Intelligence Driven Wireless Networks in B5G and 6G Era: A Survey[J]. ZTE Communications, 2024, 22(3): 99-105.

Figures/Tables 7

References 18

1	3rd Generation Partnership Project (3GPP). Study on enhancement for data collection for NR and EN-DC [R]. 3GPP R 37.817, 2022
2	CHEN W S, LIN X Q, LEE J, et al. 5G-advanced toward 6G: past, present, and future [J]. IEEE journal on selected areas in communications, 2023, 41(6): 1592–1619. DOI: 10.1109/JSAC.2023.3274037
3	3rd Generation Partnership Project (3GPP). Study on artificial intelligence (AI)/machine learning (ML) for NR air interface [R]. 3GPP TR 37.843, 2023
4	BOUTIBA K, BAGAA M, KSENTINI A. Radio link failure prediction in 5G networks [C]//Proc. IEEE Global Communications Conference (GLOBECOM). IEEE, 2021: 1–6. DOI: 10.1109/GLOBECOM46510.2021.9685587
5	BUTT M M, PANTELIDOU A, KOVÁCS I Z. ML-assisted UE positioning: performance analysis and 5G architecture enhancements [J]. IEEE open journal of vehicular technology, 2021, 2: 377–388. DOI: 10.1109/OJVT.2021.3110134
6	DEBBABI F, JMAL R, CHAARI L, et al. Overview of AI-based algorithms for network slicing resource management in B5G and 6G [C]//Proc. International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2022: 330–335. DOI: 10.1109/IWCMC55113.2022.9824988
7	LARSEN L M P, CHRISTIANSEN H L, RUEPP S, et al. Toward greener 5G and beyond radio access networks: a survey [J]. IEEE open journal of the communications society, 2023, 4: 768–797. DOI: 10.1109/OJCOMS.2023.3257889
8	TAN R M, WU T, SHI Y, et al. Intelligent energy saving solution of 5G base station based on artificial intelligence technologies [C]//Proc. IEEE International Joint EMC/SI/PI and EMC Europe Symposium. IEEE, 2021: 739–742. DOI: 10.1109/EMC/SI/PI/EMCEurope52599.2021.9559261
9	GAO Y, CHEN J J, LIU Z, et al. Machine learning based energy saving scheme in wireless access networks [C]//Proc. International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2020: 1573–1578. DOI: 10.1109/IWCMC48107.2020.9148536
10	CHEN J J, GAO Y, LIU Z, et al. Future vision on artificial intelligence assisted green energy efficiency network [J]. ZTE communications, 2023, 21 (2): 34–39. DOI: 10.12142/ZTECOM.202302006
11	GIANNOPOULOS A, SPANTIDEAS S, KAPSALIS N, et al. Supporting intelligence in disaggregated open radio access networks: architectural principles, AI/ML workflow, and use cases [J]. IEEE access, 2022, 10: 39580–39595. DOI: 10.1109/ACCESS.2022.3166160
12	TAN R M, SHI Y, FAN Y Y, et al. Energy saving technologies and best practices for 5G radio access network [J]. IEEE access, 2022, 10: 51747–51756. DOI: 10.1109/ACCESS.2022.3174089
13	SHRIVASTAVA P, PATEL S. Selection of efficient and accurate prediction algorithm for employing real time 5G data load prediction [C]//Proc. IEEE 6th International Conference on Computing, Communication and Automation (ICCCA). IEEE, 2021: 572–580. DOI: 10.1109/ICCCA52192.2021.9666235
14	NAN J H, AI M, LIU A J, et al. Regional-union based federated learning for wireless traffic prediction in 5G-Advanced/6G network [C]//Proc. IEEE/CIC International Conference on Communications in China (ICCC Workshops). IEEE, 2022: 423–427. DOI: 10.1109/ICCCWorkshops55477.2022.9896702
15	GAO Y, ZHANG M, CHEN J J, et al. Accurate load prediction algorithms assisted with machine learning for network traffic [C]//Proceedings of International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2021: 1683–1688. DOI: 10.1109/IWCMC51323.2021.9498910
16	WANG C, ZHAO Z, SUN Q, et al. Deep learning-based intelligent dual connectivity for mobility management in dense network [C]//IEEE 88th Vehicular Technology Conference (VTC-Fall). IEEE, 2018: 1–5. DOI: 10.1109/VTCFall.2018.8690554
17	GAO Y, CHEN J J, LIU Z, et al. Deep learning based location prediction with multiple features in communication network [C]//Proc.IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2021: 1–5. DOI: 10.1109/WCNC49053.2021.9417278
18	WANG W H, DUAN X Y, SUN W F, et al. Research on mobility prediction in 5G and beyond for vertical industries [C]//Proc. IEEE/CIC International Conference on Communications in China (ICCC Workshops). IEEE, 2021: 379–383. DOI: 10.1109/ICCCWorkshops52231.2021.9538889

Intelligence Driven Wireless Networks in B5G and 6G Era: A Survey

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 18

Related Articles 1

Recommended Articles

Metrics