A Case Study on Intelligent Operation System for Wireless Networks

A Case Study on Intelligent Operation System for Wireless Networks

LIU Jianwei, YUAN Yifei, and HAN Jing

(ZTE Corporation, Shenzhen, Guangdong 518057, China)

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.
Keywords: 5G; self-organizing network; machine learning; anomaly detection; fault diagnosis