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

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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)

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

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

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References 15

1	MITRA R N,AGRAWAL D P.5G Mobile Technology: A Survey [J].ICT Express,2015,1(3):132–137.DOI:10.1016/j.icte.2016.01.003 doi: 10.1016/j.icte.2016.01.003
2	C⁃LI,HAN S,XU Z,et al.New Paradigm of 5G Wireless Internet [J].IEEE Journal on Selected Areas in Communications,2016,34(3):474–482.DOI:10.1109/JSAC.2016.2525739 doi: 10.1109/JSAC.2016.2525739
3	PARK T,ABUZAINAB N,SAAD W.Learning How to Communicate in the Internet of Things: Finite Resources and Heterogeneity [J].IEEE Access,2016,4:7063–7073.DOI:10.1109/ACCESS.2016.2615643 doi: 10.1109/ACCESS.2016.2615643
4	SOLDANI D,MANZALINI A.Horizon 2020 and Beyond: On the 5G Operating System for a True Digital Society [J].IEEE Vehicular Technology Magazine,2015,10(1):32–42.DOI:10.1109/MVT.2014.2380581 doi: 10.1109/MVT.2014.2380581
5	IMRAN A,ZOHA A,ABU⁃DAYYA A.Challenges in 5G: How to Empower SON with Big Data for Enabling 5G [J].IEEE Network,2014,28(6):27–33.DOI:10.1109/MNET.2014.6963801 doi: 10.1109/MNET.2014.6963801
6	KIBRIA M G,NGUYEN K,VILLARDI G P,et al.Big Data Analytics, Machine Learning, and Artificial Intelligence in Next⁃Generation Wireless Networks [J].IEEE Access,2018,6:32328–32338.DOI:10.1109/ACCESS.2018.2837692 doi: 10.1109/ACCESS.2018.2837692
7	LONG F,LI N,WANG Y X.Autonomic Mobile Networks: the Use of Artificial Intelligence in Wireless Communications [C]//2nd International Conference on Advanced Robotics and Mechatronics (ICARM).New York, USA:IEEE,2017:582–586.DOI:10.1109/ICARM.2017.8273227 doi: 10.1109/ICARM.2017.8273227
8	JIANG C X,ZHANG H J,REN Y,et al.Machine Learning Paradigms for Next⁃Generation Wireless Networks [J].IEEE Wireless Communications,2016,24(2):98–105.DOI:10.1109/MWC.2016.1500356WC doi: 10.1109/MWC.2016.1500356WC
9	FENG V⁃S,CHANG S Y.Determination of Wireless Networks Parameters Through Parallel Hierarchical Support Vector Machines [J].IEEE Transactions on Parallel and Distributed Systems,2011,23(3):505–512.DOI:10.1109/TPDS.2011.156 doi: 10.1109/TPDS.2011.156
10	XIA M,OWADA Y,INOUE M,et al.Optical and Wireless Hybrid Access Networks: Design and Optimization [J].Journal of Optical Communications and Networking,2012,4(10):749–759.DOI:10.1364/JOCN.4.000749 doi: 10.1364/JOCN.4.000749
11	ALNWAIMI G,VAHID S,MOESSNER K.Dynamic Heterogeneous Learning Games for Opportunistic Access in LTE⁃Based Macro/Femtocell Deployments [J].IEEE Transactions on Wireless Communications,2014,14(4):2294–2308.DOI:10.1109/TWC.2014.2384510 doi: 10.1109/TWC.2014.2384510
12	MENG Z H,LIU J W,HAN J.Time Series Clustering Based on Structural Features [J].ZTE Technology Journal,2018,24(03):61–66.DOI:10.3969/j.issn.1009⁃6868.2018.03.011 doi: 10.3969/j.issn.1009?6868.2018.03.011
13	LAPTEV N,AMIZADEH S,FLINT I.Generic and Scalable Framework for Automated Time⁃Series Anomaly Detection [C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York, USA:ACM,2015:1939–1947.DOI:10.1145/2783258.2788611 doi: 10.1145/2783258.2788611
14	LI G,QIN S⁃Z,JI Y⁃D,et al.Total PLS Based Contribution Plots for Fault Diagnosis [J].Acta Automatica Sinica,2009,35(6):759–765.DOI:10.1016/S1874⁃1029(08)60094⁃4 doi: 10.1016/S1874?1029(08)60094?4
15	DANG Y N,LIN Q W,HUANG P.AIOps: Real⁃World Challenges and Research Innovations[C]//Proceedings of the 41st International Conference on Software Engineering: Companion Proceedings.New York, USA:IEEE Press,2019:4–5.DOI:10.1109/ICSE-Companion.2019.00023 doi: 10.1109/ICSE-Companion.2019.00023

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A Case Study on Intelligent Operation System for Wireless Networks

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