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5G Network Management System With Machine Learning Based Analytics
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AI
DOI：10.1109/ACCESS.2022.3190372.png)
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摘要
En 中文
Application of intelligent data analytics using machine learning in management of 5G networks can enable autonomous networking capabilities in 5G networks. This paper describes the design and implementation of CygNet MaSoN, a management system supporting advanced aggregation and analytics features combined with machine learning. The system supports detection of anomalous network behaviour, detection of degradation in network performance and service quality and also supports resource optimization. The main objective is to achieve self-organizing and closed loop automation functionalities expected as part of autonomous functioning of 5G networks. Details of the system architecture and components are presented. Three real-life use cases implemented on this system are then described. Machine learning models built and synthetic data generation methods adopted are presented with the features considered. The results obtained using the MaSoN system are also presented to demonstrate the effectiveness of the system in 5G network operations.
Keyword:
5G mobile communication
Machine learning
Computer architecture
Servers
Automation
Predictive models
Data models
5G network management
autonomous networking
closed loop automation
data analytics
machine learning
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
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A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges应用于软件定义网络 (SDN) 的机器学习技术综述: 研究问题与挑战
PREAI
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