Machine Learning for Network Slicing Resource Management: A Comprehensive Survey

January 22, 2020 ยท The Cartographer ยท ๐Ÿ› arXiv.org

๐Ÿ“š THE CARTOGRAPHER: The Cartographer
Survey/review paper โ€” maps the landscape rather than implementing a method.

"No code URL or promise found in abstract"
"Title-pattern auto-detect: Machine Learning for Network Slicing Resource Management: A Comprehensive Survey"

Evidence collected by the PWNC Scanner

Authors Bin Han, Hans D. Schotten arXiv ID 2001.07974 Category cs.NI: Networking & Internet Cross-listed cs.LG Citations 28 Venue arXiv.org Last Checked 2 days ago
Abstract
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.
Community shame:
Not yet rated
Community Contributions

Found the code? Know the venue? Think something is wrong? Let us know!

๐Ÿ“œ Similar Papers

In the same crypt โ€” Networking & Internet