From 4G to 5G: Self-organized network management meets machine learning

• Published in: Computer communications 2018-09, Vol.129, p.248-268
• Main Authors: Moysen, Jessica, Giupponi, Lorenza
• Format: Article
• Language: English
• Subjects: Big data; Machine learning; Mobile networks; Network management; Self-organizing networks
• ISSN: 0140-3664; 1873-703X
• DOI: 10.1016/j.comcom.2018.07.015

Self-organization as applied to cellular networks is usually referred to Selforganizing Networks (SONs), and it is a key driver for improving Operations,Administration, and Management (OAM) activities. SON aims at reducing the cost of installation and management of 4G and future 5G networks, by simplifying operational tasks through the capability to configure, optimize and heal itself. To satisfy 5G network management requirements, this autonomous management vision has to be extended to the end to end network. In literature and also in some instances of products available in the market, Machine Learning (ML) has been identified as the key tool to implement autonomous adaptability and take advantage of experience when making decisions. In this paper, we survey how 5G network management, with an end-to-end perspective of the network, can significantly benefit from ML solutions. We review and provide the basic concepts and taxonomy for SON, network management and ML. We analyze the available state of the art in the literature, standardization, and in the market. We pay special attention to 3rd Generation Partnership Project (3GPP) evolution in the area of network management and to the data that can be extracted from 3GPP networks, in order to gain knowledge and experience in how the network is working, and improve network performance in a proactive way. Finally, we go through the main challenges associated with this line of research, in both 4G and in what 5G is getting designed, while identifying new directions for research.