Future Vision on Artificial Intelligence Assisted Green Energy Efficiency Network

doi:10.12142/ZTECOM.202302006

ZTE Communications ›› 2023, Vol. 21 ›› Issue (2): 34-39.DOI: 10.12142/ZTECOM.202302006

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Future Vision on Artificial Intelligence Assisted Green Energy Efficiency Network

CHEN Jiajun^1,²(), GAO Yin^1,², LIU Zhuang², LI Dapeng^1,²

^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:2023-02-01 Online:2023-06-13 Published:2023-06-13
About author:Chen Jiajun (chen.jiajun1@zte.com.cn) 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 State Key Laboratory of Mobile Network and Mobile Multimedia Technology, China. His research interests include 5G wireless communications and artificial intelligence.|GAO Yin received her master’s degree in circuit and system from Xidian University, China in 2005. Since then she has been engaged in 4G/5G technologies and is currenly working as 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 (co)-authored hundreds of proposals for 3GPP meeting and journal papers in wireless communications and has filed more than 200 patents. She has been served as RAN3 Vice Chair of 3GPP RAN3 since 2017 and was elected as Chair of 3GPP RAN3 in May 2021.|LIU Zhuang received his master’s degree in computer science from Xidian University, China in 2003. He is currently a senior 5G research engineer at the R&D center of ZTE Corporation and the State Key Laboratory of Mobile Network and Mobile Multimedia, China. His research interests include 5G wireless communications and signal processing. He has filed more than 100 patents and submitted several hundreds of 3GPP contributions.|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 in ZTE corporation and mainly focuses on research and implementation of the wireless access network system.

Abstract

Abstract:

To meet the key performance requirement of the 5G network and the demand of the growing number of mobile subscribers, millions of base stations are being constructed. 5G New Radio is designed to enable denser network deployments, which raises significant concerns about network energy consumption. Machine learning (ML), as a kind of artificial intelligence (AI) technologies, can enhance network optimization performance and energy efficiency. In this paper, we propose AI/ML-assisted energy-saving strategies to achieve optimal performance in terms of cell shutdown duration and energy efficiency. To realize network intelligence, we put forward the concept of intrinsic AI, which integrates AI into every aspect of wireless communication networks.

Key words: machine learning, energy efficiency, traffic distribution, load prediction, intrinsic AI

CHEN Jiajun, GAO Yin, LIU Zhuang, LI Dapeng. Future Vision on Artificial Intelligence Assisted Green Energy Efficiency Network[J]. ZTE Communications, 2023, 21(2): 34-39.

Figures/Tables 8

References 11

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Future Vision on Artificial Intelligence Assisted Green Energy Efficiency Network

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