• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Wireless Communications and Mobile Computing Conference >Deep reinforcement learning-based resource reservation method for Power Emergency Internet-of-things Slice
【24h】

Deep reinforcement learning-based resource reservation method for Power Emergency Internet-of-things Slice

机译：基于深度加强学习的资源预留方法，用于电源紧急互联网切片

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Aiming at the ultra-low latency service demand of power emergency Internet of Things (PEIoT), a multi-slice network architecture for ultra-low delay transmission of emergency Internet of Things was designed, and a PEIoT slice resource reservation and multi-heterogeneous slice resource sharing framework was proposed. The proposed framework adopts the deep reinforcement learning method to realize the automatic prediction and allocation of real-time resource requirements among heterogeneous slices. Simulation results show that the method based on resource reservation enables PEIoT slice to explicitly retain resources and provides a better level of security isolation. Deep reinforcement learning can ensure the accurate and real-time update of resource reservation and effectively consider the resource utilization rate and the differentiated service quality requirements of slices. The comparison with two existing algorithms shows that Dueling DQN has better performance advantages.
机译：针对超低延迟服务的电源紧急互联网（PEIT），设计了一种用于紧急情况互联网的超低延迟传输的多切片网络架构，以及PEIOT切片资源预留和多异常切片 提出了资源共享框架。 所提出的框架采用深度加强学习方法来实现异构切片之间的自动预测和分配实时资源需求。 仿真结果表明，基于资源预留的方法使PEIOT切片明确地保留资源并提供更好的安全隔离级别。 深度加固学习可以确保资源预留的准确性和实时更新，并有效地考虑资源利用率和切片的差异化服务质量要求。 与两个现有算法的比较显示，Dueling DQN具有更好的性能优势。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号