东北大学学报（自然科学版）

东北大学学报（自然科学版） ›› 2023, Vol. 44 ›› Issue (11): 1521-1528.DOI: 10.12068/j.issn.1005-3026.2023.11.001

• 信息与控制 •    下一篇

时变信道下基于LSTM的信道估计方法

季策1, 2， 王鑫1， 耿蓉1， 梁敏骏3   

  1. (1. 东北大学 计算机科学与工程学院， 辽宁 沈阳110169； 2. 东北大学 医学影像智能计算教育部重点实验室， 辽宁 沈阳110169； 3. 东北大学 信息科学与工程学院， 辽宁 沈阳110819)
  • 发布日期:2023-12-05
  • 通讯作者: 季策
  • 作者简介:季策(1969－)，女，辽宁沈阳人，东北大学副教授.
  • 基金资助:
    中央高校基本科研业务费专项资金资助项目(N2116015， N2116020).

LSTM-Based Channel Estimation Method in Time-Varying Channels

JI Ce1,2， WANG Xin1， GENG Rong1， LIANG Min-jun3   

  1. 1. School of Computer Science & Engineering， Northeastern University， Shenyang 110169， China; 2. Key Laboratory of Intelligent Computing in Medical Image， Ministry of Education， Northeastern University， Shenyang 110169， China; 3. School of Information Science & Engineering， Northeastern University， Shenyang 110819， China.
  • Published:2023-12-05
  • Contact: WANG Xin
  • About author:-
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摘要： 针对时变信道环境下传统信道估计方法性能受限，其他基于深度学习的信道估计方法估计精度低或复杂度高的问题，提出一种基于长短期记忆结构的信道估计网络，由双向长短期记忆(bidirectional long short-term memory， BiLSTM)网络和多层感知器(multilayer perceptron， MLP)网络组成，即BiLSTM-MLP.首先，利用BiLSTM网络来学习信道的时变特性；然后，利用MLP网络进行去噪并重构信道估计.仿真结果表明，所提出的信道估计方法与传统方法相比，性能提升明显，与同类型的基于深度学习的估计方法相比，复杂度较低且性能更优.此外，所提方法还具有对不同导频密度的鲁棒性.

关键词: 时变信道；信道估计；深度学习；长短期记忆； 多层感知器

Abstract: Aiming to address the limitations of traditional channel estimation methods in time-varying channel environments， as well as the low estimation accuracy or high complexity of deep learning-based channel estimation methods， a channel estimation network based on long short-term memory structure is proposed， which consists of a bidirectional long short-term memory(BiLSTM)network and a multilayer perceptron(MLP)network， namely BiLSTM-MLP. First， the BiLSTM network is used to learn the time-varying characteristics of the channel. Then， a MLP network is used to denoise and reconstruct the channel estimation. Simulation results show that the proposed channel estimation method has better performance than traditional methods， and has lower complexity and better performance compared with the same type of deep learning-based estimation methods. Furthermore， the proposed method is also robust to different pilot densities.

Key words: time-varying channel; channel estimation; deep learning; long short-term memory (LSTM); multilayer perceptron (MLP)

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