The emerging technology of multi-tenancy network slicing is considered as an essential feature of 5G cellular networks. It provides network slices as a new type of public cloud services, and therewith increases the service flexibility and enhances the network resource efficiency. Meanwhile, it raises new challenges of network resource management. A number of various methods have been proposed over the recent past years, in which machine learning and artificial intelligence techniques are widely deployed. In this article, we provide a survey to existing approaches of network slicing resource management, with a highlight on the roles played by machine learning in them.
@article{arxiv.2001.07974,
title = {Machine Learning for Network Slicing Resource Management: A Comprehensive Survey},
author = {Bin Han and Hans D. Schotten},
journal= {arXiv preprint arXiv:2001.07974},
year = {2021}
}