Lightweight and Low Complexity CSI Feedback Method for FDD Massive MIMO Systems

LIAO Yong; LI Yu-jie

doi:10.12263/DZXB.20210723

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Lightweight and Low Complexity CSI Feedback Method for FDD Massive MIMO Systems

PAPERS | 更新时间：2025-12-08

- Lightweight and Low Complexity CSI Feedback Method for FDD Massive MIMO Systems
- ACTA ELECTRONICA SINICA Vol. 50, Issue 5, Pages: 1211-1217(2022)
- 作者机构：
  
  重庆大学微电子与通信工程学院，重庆 400044
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210723
  CLC： TN911.72
- Received：07 June 2021，
  
  Revised：2021-12-27，
  
  Published：25 May 2022
- 稿件说明：
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廖勇,李玉杰.一种轻量化低复杂度的FDD大规模MIMO系统CSI反馈方法[J].电子学报,2022,50(05):1211-1217.

LIAO Yong,LI Yu-jie.Lightweight and Low Complexity CSI Feedback Method for FDD Massive MIMO Systems[J].ACTA ELECTRONICA SINICA,2022,50(05):1211-1217.
廖勇,李玉杰.一种轻量化低复杂度的FDD大规模MIMO系统CSI反馈方法[J].电子学报,2022,50(05):1211-1217. DOI： 10.12263/DZXB.20210723.

LIAO Yong,LI Yu-jie.Lightweight and Low Complexity CSI Feedback Method for FDD Massive MIMO Systems[J].ACTA ELECTRONICA SINICA,2022,50(05):1211-1217. DOI： 10.12263/DZXB.20210723.

摘要

针对频分双工大规模多输入多输出(Multiple-Input Multiple-Output，MIMO)通信系统中信道状态信息(Channel State Information，CSI)反馈方法复杂度高、精度低和开销大的问题，本文提出了一种基于深度学习的低复杂度CSI反馈方法.该方法通过端到端的方式构建了一种从用户设备编码器到基站解码器相结合的网络结构.编解码器利用连续的平均池化层和上采样层完成特征图的降维和升维，同时引入深度可分离卷积神经网络减少网络参数量.在解码器部分，本文利用残差网络构建连续的拥有大卷积核的残差块逼近原始CSI矩阵.仿真结果表明，和已有的代表性方法相比，本文所提方法在归一化均方误差上有2dB~5dB的性能提升，在余弦相似度上也有2%~5%的提升，并且在时间复杂度和空间复杂度上均有更好的表现.

Abstract

Aiming at the problems of high complexity

low precision and high overhead of the channel state information(CSI) feedback method in frequency division duplexing massive multiple input multiple output(MIMO) communication system

this paper proposes a low complexity CSI feedback method based on deep learning. This method constructs a network structure from the encoder of user equipment to the decoder of base station in an end-to-end way. The codec uses a continuous average pooling layers and up sampling layers to complete the dimensionality reduction and dimensionality increase of the feature map

and introduces a depthwise separable convolutional neural network to reduce the amount of network parameters. In the decoder part

this paper uses the residual network to construct continuous residual blocks with large convolution kernel to approximate the original CSI matrix. Simulation results show that

compared with the existing representative methods

the method proposed in this paper has 2dB~5dB improvement in normalized mean square error

and 2%~5% improvement in cosine similarity

and it has better performance in time complexity and space complexity.

关键词

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references

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