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一个E2E网络切片框架用于使用机器学习创建和部署切片.

Sujitha Venkatapathy1, Thiruvenkadam Srinivasan2, Han-Gue Jo3

  • 1TIFAC-CORE in Cyber Security, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, Tamil Nadu, India.

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概括
此摘要是机器生成的。

这项研究使用机器学习来创建5G的网络切片,优化对mMTC,eMBB和uRLLC等服务的资源配置. 该方法提高了用户访问和资源效率,同时减少了带宽使用.

关键词:
5G网络中的5G网络.机器学习是机器学习.网络切片是指网络的切片.虚拟网络嵌入式嵌入式虚拟网络功能 虚拟网络功能

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科学领域:

  • 电信工程 电信工程 电信工程
  • 计算机科学 计算机科学
  • 网络架构 网络架构

背景情况:

  • 5G网络需要灵活和动态的功能,可以通过网络切片实现.
  • 网络功能虚拟化 (NFV) 和软件定义网络 (SDN) 是网络切片的关键支持者.
  • 网络切片允许根据特定要求定制,端到端的孤立服务.

研究的目的:

  • 开发一种基于机器学习的方法来构建网络切片.
  • 通过使用动态编程,有效地将资源分配给这些新创建的网络切片.
  • 优化关键绩效指标 (KPI),如用户访问率和资源效率.

主要方法:

  • 构建了一个基板网络,考虑了CPU容量,带宽,延迟,链接容量和安全性等KPI.
  • 使用多层感知器 (MLP) 与ADAM优化算法生成网络切片.
  • 使用Dijkstra的算法进行了资源分配,以找到最短的路径,最大限度地提高用户访问和资源效率.

主要成果:

  • 拟议的模型有效地对不同服务 (mMTC,eMBB,uRLLC) 的网络切片进行分类.
  • 最佳的网络切片被分配给所要求的服务.
  • 该模型显示了高资源效率和降低总带宽利用率.

结论:

  • 开发的方法成功地构建和分配了5G网络中网络切片的资源.
  • 机器学习和动态编程的整合导致了高效的资源管理.
  • 这种方法为提高5G网络性能和定制提供了一个有希望的解决方案.