Enabling Energy Efficiency in 5G Network
LIU Zhuang1,2, GAO Yin1,2, 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)
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.
cell switch off; energy efficiency; energy saving; 5G; machine learning