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Machine Learning for Network Slicing Resource Management: A Comprehensive Survey

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arxiv 2001.07974 v1 pith:VTATBC6L submitted 2020-01-22 cs.NI cs.LG

Machine Learning for Network Slicing Resource Management: A Comprehensive Survey

classification cs.NI cs.LG
keywords networkresourcelearningmachinemanagementslicingsurveyapproaches
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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