论文标题
网络切片资源管理的机器学习：一项全面调查
Machine Learning for Network Slicing Resource Management: A Comprehensive Survey
论文作者
论文摘要
多租期网络切片的新兴技术被认为是5G蜂窝网络的基本特征。它提供网络切片作为一种新型的公共云服务类型，因此可以提高服务灵活性并提高网络资源效率。同时，它提出了网络资源管理的新挑战。在过去几年中，已经提出了许多各种方法，其中机器学习和人工智能技术被广泛部署。在本文中，我们对网络切片资源管理的现有方法进行了调查，并重点介绍了机器学习中的角色。
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.
