基于深度强化学习的OFDM自适应导频设计

刘乔寿; 周雄; 刘爽; 邓义锋

doi:10.11959/j.issn.1000-436x.2023169

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基于深度强化学习的OFDM自适应导频设计

学术论文 | 更新时间：2024-06-06

- 基于深度强化学习的OFDM自适应导频设计
- Adaptive pilot design for OFDM based on deep reinforcement learning
- 通信学报 2023年44卷第9期页码：104-114
- 作者机构：
  
  1. 重庆邮电大学通信与信息工程学院，重庆 400065
  2. 先进网络与智能互联技术重庆市高校重点实验室，重庆 400065
  3. 泛在感知与互联重庆市重点实验室，重庆 400065
- 作者简介：
  
  [ "刘乔寿（1979- ），男，云南曲靖人，博士，重庆邮电大学副教授、硕士生导师，主要研究方向为5G超密集网络干扰协调、云边协同智能计算、FPGA智能算法加速、物联网系统及终端设备开发" ]
  [ "周雄（1999- ），男，湖北荆州人，重庆邮电大学硕士生，主要研究方向为基于深度强化学习的信道估计" ]
  [ "刘爽（2000- ），男，安徽桐城人，重庆邮电大学硕士生，主要研究方向为空中计算" ]
  [ "邓义锋（1999- ），男，四川成都人，重庆邮电大学硕士生，主要研究方向为空中计算" ]
- 基金信息：
  
  国家自然科学基金资助项目(61901075);重庆市教委科学技术基金资助项目(KJZDK202200604)
- DOI：10.11959/j.issn.1000-436x.2023169
  中图分类号： TN92
- 网络出版日期：2023-09，
  
  纸质出版日期：2023-09-25
- 稿件说明：
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刘乔寿, 周雄, 刘爽, 等. 基于深度强化学习的OFDM自适应导频设计[J]. 通信学报, 2023,44(9):104-114.

Qiaoshou LIU, Xiong ZHOU, Shuang LIU, et al. Adaptive pilot design for OFDM based on deep reinforcement learning[J]. Journal on communications, 2023, 44(9): 104-114.
刘乔寿, 周雄, 刘爽, 等. 基于深度强化学习的OFDM自适应导频设计[J]. 通信学报, 2023,44(9):104-114. DOI： 10.11959/j.issn.1000-436x.2023169.

Qiaoshou LIU, Xiong ZHOU, Shuang LIU, et al. Adaptive pilot design for OFDM based on deep reinforcement learning[J]. Journal on communications, 2023, 44(9): 104-114. DOI： 10.11959/j.issn.1000-436x.2023169.

摘要

针对正交频分复用系统，提出了一种基于深度强化学习的自适应导频设计算法。将导频设计问题映射为马尔可夫决策过程，导频位置的索引定义为动作，用基于减少均方误差的策略定义奖励函数，使用深度强化学习来更新导频位置。根据信道条件自适应地动态分配导频，从而利用信道特性对抗信道衰落。仿真结果表明，所提算法在3GPP的3种典型多径信道下相较于传统导频均匀分配方案信道估计性能有显著的提升。

Abstract

For orthogonal frequency division multiplexing (OFDM) systems

an adaptive pilot design algorithm based on deep reinforcement learning was proposed.The pilot design problem was formulated as a Markov decision process

where the index of pilot positions was defined as actions.A reward function based on mean squared error (MSE) reduction strategy was formulated

and deep reinforcement learning was employed to update the pilot positions.The pilot was adaptively and dynamically allocated based on channel conditions

thereby utilizing channel characteristics to combat channel fading.The simulation results show that the proposed algorithm has significantly improved channel estimation performance compared with the traditional pilot uniform allocation scheme under three typical multipath channels of 3GPP.

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references

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