• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Physical Communication >Delay-aware packet scheduling for massive MIMO beamforming transmission using large-scale reinforcement learning
【24h】

Delay-aware packet scheduling for massive MIMO beamforming transmission using large-scale reinforcement learning

机译：使用大规模强化学习的大规模MIMO波束成形传输的延迟感知分组调度

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

This work addresses the massive multiple input multiple output (MIMO) beamforming scheduling problem. The scheduling model for packet transmission with paralleled caches using massive MIMO multicast beamforming is proposed by using reinforcement learning (RL) theory to denote the mapping function from the system state to the beamforming strategy. The delay-aware model-free RL scheduling problem for massive MIMO multicast beamforming is derived and analyzed in asymptotic condition. We show that the model-free RL method can be a complementary way to optimize the packet delay when the channel state information (CSI) is unavailable and the traditional convex optimization based methods are consequently ineffective. To derive low-complexity algorithm, we build the RL sub-problems for each specific multicast group which are asymptotically independent. The policy gradient method is used to solve the proposed RL problems. In numerical experiments we provide simulation results under different number of transmitting antennas and provide performance comparison between the proposed method and randomized method. It shows that even without using CSI, the RL based policy can still dynamically optimize the delay of the system and out-perform the randomized policy. (C) 2018 Elsevier B.V. All rights reserved.
机译：这项工作解决了大规模多输入多输出（MIMO）波束成形调度问题。利用强化学习（RL）理论来表示从系统状态到波束赋形策略的映射函数，提出了利用大规模MIMO多播波束赋形的并行缓存分组传输调度模型。在渐近条件下，导出并分析了用于大规模MIMO多播波束成形的无延迟感知的无模型RL调度问题。我们表明，当信道状态信息（CSI）不可用并且传统的基于凸优化的方法因此无效时，无模型RL方法可以作为优化数据包延迟的补充方法。为了导出低复杂度算法，我们为每个特定的多播组建立了渐近独立的RL子问题。策略梯度法用于解决所提出的RL问题。在数值实验中，我们提供了在不同数量的发射天线下的仿真结果，并提供了所提方法与随机方法之间的性能比较。它表明，即使不使用CSI，基于RL的策略仍然可以动态优化系统的延迟，并且性能优于随机策略。 （C）2018 Elsevier B.V.保留所有权利。

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