A Case Study on Intelligent Operation System for Wireless Networks

doi:10.12142/ZTECOM.201904004

ZTE Communications ›› 2019, Vol. 17 ›› Issue (4): 19-26.DOI: 10.12142/ZTECOM.201904004

收稿日期:2019-09-30 出版日期:2019-12-25 发布日期:2020-04-16

A Case Study on Intelligent Operation System for Wireless Networks

LIU Jianwei(), YUAN Yifei, HAN Jing

ZTE Corporation, Shenzhen, Guangdong 518057, China

Received:2019-09-30 Online:2019-12-25 Published:2020-04-16
About author:LIU Jianwei(liu.jianweizp@zte.com.cn)received the B.S. degree from the School of Mechanical Science Engineering, Huazhong University of Science Technology, China in 2010, and the Ph.D. degree in engineering from Shanghai Jiao Tong University, China in 2016. Since 2016, he has joined ZTE Corporation, working on intelligent operation of cloud platforms and wireless networks. His research interests include machine learning, data mining, and signal processing. He has published over 10 peer-reviewed papers in reputed international journals and conferences.|YUAN Yifeireceived his bachelor’s and master’s degrees from Tsinghua University, China, and Ph.D. from Carnegie Mellon University, USA. He was with Alcatel-Lucent from 2000 to 2008, working on 3G/4G key technologies. Since 2008, he has been with ZTE Corporation as a technical director and chief engineer responsible for the research of standards on LTE-Advanced and 5G. His research interests include MIMO, channel coding, non-orthogonal multiple access (NOMA), and IoT. He was admitted to Thousand Talent Plan Program of China. He has extensive publications, including five books on LTE and 5G. He has over 50 granted patents. He is the rapporteur of NOMA study item in 3GPP.|HAN Jingreceived her master’s degree from Nanjing University of Aeronautics and Astronautics of China. She has been with ZTE Corporation since 2000; she worked there on 3G/4G key technologies from 2000 to 2016 and has become a technical director responsible for intelligent operation of cloud platforms and wireless networks since 2016. Her research interests include KPI anomaly detection model, prediction model of cell traffic, RCA, and self-optimization of parameters.
Supported by:
Shanghai Sailing Program(18YF1423300)

摘要/Abstract

Abstract:

The emerging fifth generation (5G) network has the potential to satisfy the rapidly growing traffic demand and promote the transformation of smartphone-centric networks into an Internet of Things (IoT) ecosystem. Due to the introduction of new communication technologies and the increased density of 5G cells, the complexity of operation and operational expenditure (OPEX) will become very challenging in 5G. Self-organizing network (SON) has been researched extensively since 2G, to cope with the similar challenge, however by predefined policies, rather than intelligent analysis. The requirement for better quality of experience and the complexity of 5G network demands call for an approach that is different from SON. In several recent studies, the combination of machine learning (ML) technology with SON has been investigated. In this paper, we focus on the intelligent operation of wireless network through ML algorithms. A comprehensive and flexible framework is proposed to achieve an intelligent operation system. Two use cases are also studied to use ML algorithms to automate the anomaly detection and fault diagnosis of key performance indicators (KPIs) in wireless networks. The effectiveness of the proposed ML algorithms is demonstrated by the real data experiments, thus encouraging the further research for intelligent wireless network operation.

Key words: 5G, self-organizing network, machine learning, anomaly detection, fault diagnosis

. [J]. ZTE Communications, 2019, 17(4): 19-26.

LIU Jianwei, YUAN Yifei, HAN Jing. A Case Study on Intelligent Operation System for Wireless Networks[J]. ZTE Communications, 2019, 17(4): 19-26.

图/表 8

Figure 1 Three categories of machine learning algorithms and typical methods in each category.

Figure 2 A general overview of intelligent operation system.

Figure 3 An illustration of machine learning (ML)-based anomaly detection method

Figure 4 A demonstration of two-stage time series modeling method.

Figure 5 An illustration of time series modeling by Holt-Winters: (a) represents the true value (blue curve) and fitting value (green curve) in historical data; (b) represents the true value (blue curve) and the predicted thresholds (red curve) in the following day.

Figure 6 A mixed scheme that combines the rule-based and ML-based diagnosis modules for KPI anomaly diagnosis.

Figure 7 Root cause analysis with PLS model when a KPI is abnormal.

Figure 8 An experimental example of partial least squares regression (PLS) method for root cause analysis.

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