Machine Learning-Based Framework for Autonomous Network Management in 5G SystemsWei Jiang; Mathias Strufe; Hans Dieter Schotten
In: IEEE. European Conference on Networks and Communications (EuCNC-2018), June 18-21, Ljubljana, Slovenia, IEEE, 2018.
To meet the radical technical requirements specified by ITU-R IMT-2020, the fifth Generation (5G) mobile system will become more complicated and heterogeneous. It imposes a great challenge on today's network managing approaches, which are already costly, vulnerable, time-consuming and therefore inapplicable to the 5G system. By applying machine learning, a possibility on autonomically self-organizing 5G networks is opened. With minimal human interventions, autonomic management can lower operational expenditure, improve user's experience and shorten time-to-market of new services. In this paper, the concept of intelligence slicing, a flexible and scalable framework for applying machine learning to enable self-organizing 5G networks, is proposed. The life-cycle management of intelligence slices, as well as intelligence domain that is defined as the effective area of a slice, are discussed. Moreover, a proof-of-concept experiment upon a wireless network test-bed is illustrated.