Enabling Energy Efficiency in 5G Network

doi:10.12142/ZTECOM.202101004

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

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Enabling Energy Efficiency in 5G Network

LIU Zhuang^1,²(), GAO Yin^1,², LI Dapeng¹, CHEN Jiajun¹, HAN Jiren¹

^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.

Abstract

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

LIU Zhuang, GAO Yin, LI Dapeng, CHEN Jiajun, HAN Jiren. Enabling Energy Efficiency in 5G Network[J]. ZTE Communications, 2021, 19(1): 20-29.

Figures/Tables 19

Figure 1 Capacity booster cell overlaid by coverage providing cell

Table 1. 5 G intra-system energy saving scenarios (only connected with 5GC)

5G Intra-System ES Scenario	Coverage Provider	Capacity Booster Provider	Description
1	gNB connected with 5GC	gNB connected with 5GC	intra-RAT ES
2	ng-eNB connected with 5GC	ng-eNB connected with 5GC	intra-RAT ES
3	gNB connected with 5GC	ng-eNB connected with 5GC	inter-RAT ES
4	ng-eNB connected with 5GC	gNB connected with 5GC	inter-RAT ES

Figure 2 5G Intra-RAT energy saving

Figure 3 5G Inter-RAT energy saving

Figure 4 NG-RAN node informs the neighbor NG-RAN node about cell status over Xn

Figure 5 Coverage NG-RAN node requests to activate booster cells over Xn

Figure 6 EN-DC energy saving

Figure 7 EN-DC energy saving signaling over X2

Figure 8 Users in China continued to move from 2G and 3G networks to 4G networks

Table 2 The inter-system ES scenarios of 4G and 5G systems (involving EPC and 5GC)

Scenario	Coverage Provider	Capacity Booster Provider
1	eNB connected with EPC	gNB connected with 5GC
2	eNB connected with EPC	ng-eNB connected with 5GC

Figure 9 Inter-system energy saving of 4G and 5G systems

Figure 10 Inter-system energy saving is not supported by signaling defined by 3GPP Release 15

Figure 11 Next-generation radio access network (NG-RAN) node connected with 5GC informs cell status to Long Term Evolution (LTE) eNB connected with evolved packet core (EPC) network

Figure 12 LTE eNB connected with EPC requests to activate an NR CELL connected with 5GC

Figure 13 CU/DU energy saving signaling support over F1 interface

Figure 14 Load prediction by using different models

Table 3 Comparison and analysis of the machine learning models

Model	Accuracy	Speed	Complexity
ARIMA	Medium	Fast	Low
Prophet	Medium	Fast	Low
LSTM	High	Slow	High
RF	High	Slow	High
Ensemble	High	Extremely slow	High

Figure 15 Statistics of the power saving (kWh)

Table 4 Comparison of power consumption and electricity charge saving with/without ES methods

Switch-off Strategy	Number of Measured Cells	Power Consumption of Measured Cells (kWh/Week)			Electricity Charge Saving of Measured Cells (CNY/Week)
Switch-off Strategy	Number of Measured Cells	No ES	Conventional ES	AI ES	Conventional ES	AI ES	Increase
Carrier	8	382	377	364	5	18	13
Carrier+symbol	7	366	344	305	22	61	39
Channel	633	16 853	16 265	15 872	588	981	393
Channel+symbol	327	8 387	7 541	6 555	846	1 832	986
Total	975	25 988	24 527	22 304	1 461	3 684	2 223

1	3GPP. Technical specification—energy efficiency of 5G release 16: TS28.310 V16.2.0 [S]. 2020
2	ZTE. Consideration on inter⁃RAT energy saving: 3GPP R3⁃191453 [R]. 2019
3	Qualcomm. Inter⁃system inter⁃RAT energy saving: 3GPP R3⁃204802 [R]. 2020
4	3GPP. Technical specification—S1 application protocol release 16: TS36.413 V16.2.0 [S]. 2020
5	3GPP. Technical specification—NG application protocol release 16: TS38.413 V16.3.0 [S]. 2020
6	3GPP. Technical specification—5G end to end key performance indicators release 16: TS 28.554 16.5.0 [S]. 2020
7	GAO Y, CHEN J, LIU Z, et al. Machine learning based energy saving scheme in wireless access networks [C]//16th International Wireless Communications and Mobile Computing Conference (IWCMC). Limassol, Cyprus: IEEE, 2020: 1573–1578

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Enabling Energy Efficiency in 5G Network

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