ZTE Communications ›› 2019, Vol. 17 ›› Issue (4): 27-32.DOI: 10.12142/ZTECOM.201904005

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Machine Learning for Network Slicing Resource Management:A Comprehensive Survey

HAN Bin1(), Hans D. SCHOTTEN1,2   

  1. 1.University of Kaiserslautern, 67663, Kaiserslautern, Germany
    2.German Research Center for Artificial Intelligence, 67663, Kaiserslautern, Germany
  • Received:2019-09-19 Online:2019-12-25 Published:2020-04-16
  • About author:HAN Bin(binhan@eit. uni-kl. de)received his B. E. degree in 2009 from Shanghai Jiao Tong University, China and his M. Sc. degree in 2012 from Darmstadt University of Technology, Germany. In 2016 he was granted the Ph. D. degree in electrical and information engineering from Kalsruhe Institute of Technology, Germany. Since July 2016 he has been with Institute of Wireless Communication, University of Kaiserslautern, Germany. His research interests are in the broad area of wireless networks and signal processing. HAN Bin has been involved in multiple European Union H2020 research projects and has published over 30 research papers.|Hans D. SCHOTTENreceived the Diplom and Ph. D. degrees in electrical engineering from RWTH Aachen University, Germany in 1990 and 1997,respectively. Since 2007, he has been Full Professor and Head of the Institute of Wireless Communication at the University of Kaiserslautern, Germany. Since 2012, he has been Scientific Director at the German Research Center for Artificial Intelligence heading the Intelligent Networks Department.


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.

Key words: 5G, machine learning, multi-tenancy, network slicing, resource management