...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Vehicular Technology >Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach
【24h】

Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach

机译：切片雾无线电接入网络中的模式选择和资源分配：强化学习方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The mode selection and resource allocation in fog radio access networks (F-RANs) have been advocated as key techniques to improve spectral and energy efficiency. In this paper, we investigate the joint optimization of mode selection and resource allocation in uplink F-RANs, where both of the traditional user equipments (UEs) and fog UEs are served by constructed network slice instances. The concerned optimization is formulated as a mixed-integer programming problem, and both the orthogonal and multiplexed subchannel allocation strategies are proposed to guarantee the slice isolation. Motivated by the development of machine learning, two reinforcement learning based algorithms are developed to solve the original high complexity problem under traditional and fog UEs' specific performance requirements. The basic idea of the proposals is to generate a good mode selection policy according to the immediate reward fed back by an environment. Simulation results validate the benefits of our proposed algorithms and show that a tradeoff between system power consumption and queue delay can be achieved.
机译：雾无线电接入网络（F-RANS）中的模式选择和资源分配已被提倡改善光谱和能量效率的关键技术。在本文中，我们调查了上行链路F-RAN中的模式选择和资源分配的联合优化，其中，两个传统的用户设备和雾UE都由构造的网络切片实例服务。将有关优化制定为混合整数编程问题，并提出了正交和多路复用子通道分配策略来保证切片隔离。通过机器学习的发展，开发了两种基于加强学习的算法，以解决传统和雾UES的特定性能要求下的原始高复杂性问题。提案的基本思想是根据环境反馈的直接奖励来生成良好的模式选择策略。仿真结果验证了我们所提出的算法的好处，并表明可以实现系统功耗和队列延迟之间的权衡。

著录项

  • 来源
    《IEEE Transactions on Vehicular Technology》 |2020年第4期|4271-4284|共14页
  • 作者

    Xiang Hongyu; Peng Mugen; Sun Yaohua; Yan Shi;

  • 作者单位

    Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China;

    Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China;

    Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China;

    Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Fog radio access network; network slicing; reinforcement learning;

    机译：雾无线电接入网络;网络切片;加固学习;

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