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ZTE Communications ›› 2021, Vol. 19 ›› Issue (1): 20-29.DOI: 10.12142/ZTECOM.202101004

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  • 收稿日期:2020-12-10 出版日期:2021-03-25 发布日期:2021-04-09

Enabling Energy Efficiency in 5G Network

LIU Zhuang1,2(), GAO Yin1,2, LI Dapeng1, CHEN Jiajun1, HAN Jiren1   

  1. 1.R&D Center of ZTE Corporation, Shanghai 201203, China
    2.State Key Laboratory of Mobile Network and Mobile Multimedia, Shenzhen 518057, China
  • Received:2020-12-10 Online:2021-03-25 Published:2021-04-09
  • About author:LIU Zhuang (liu.zhuang2@zte.com.cn) received the master’s degree in computer science from Xidian University, China in 2003. He is currently a 5G senior 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.|GAO Yin received the master’s degree in circuit and system from Xidian University, China in 2005. She has been engaged in the study of 4G/5G technology since 2005 and is currently a wireless expert and project manager at the R&D center of ZTE Corporation and the State Key Laboratory of Mobile Network and Mobile Multimedia, China. She has authored or co-authored about hundreds of proposals for 3GPP meetings and journal papers in wireless communications and has filed more than 200 patents. In August 2017, she was elected as 3GPP RAN3 Vice Chairman.|LI Dapeng received the master’s degree in computer science from University of Electronic Science and Technology of China in 2003. He is currently a senior researcher at the R&D center of ZTE Corporation and mainly focuses on research and implementation of wireless access network systems.|CHEN Jiajun received the master’s degree in electronics and communications engineering from Shanghai University, China in 2019. He has been a technology pre-research engineer at the R&D center of ZTE Corporation. His research interests include next-generation radio access network and deep learning.|HAN Jiren received the master’s degree in wireless communication systems from University of Sheffield, UK in 2016. He is currently a technology pre-research engineer at the R&D center of ZTE Corporation. His research focuses on next-generation radio access networks.

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Abstract:

The mobile Internet and Internet of Things are considered the main driving forces of 5G, as they require an ultra-dense deployment of small base stations to meet the increasing traffic demands. 5G new radio (NR) access is designed to enable denser network deployments, while leading to a significant concern about the network energy consumption. Energy consumption is a main part of network operational expense (OPEX), and base stations work as the main energy consumption equipment in the radio access network (RAN). In order to achieve RAN energy efficiency (EE), switching off cells is a strategy to reduce the energy consumption of networks during off-peak conditions. This paper introduces NR cell switching on/off schemes in 3GPP to achieve energy efficiency in 5G RAN, including intra-system energy saving (ES) scheme and inter-system ES scheme. Additionally, NR architectural features including central unit/distributed unit (CU/DU) split and dual connectivity (DC) are also considered in NR energy saving. How to apply artificial intelligence (AI) into 5G networks is a new topic in 3GPP, and we also propose a machine learning (ML) based scheme to save energy by switching off the cell selected relying on the load prediction. According to the experiment results in the real wireless environment, the ML based ES scheme can reduce more power consumption than the conventional ES scheme without load prediction.

Key words: cell switch off, energy efficiency, energy saving, 5G, machine learning