Deep learning for compressed sensing based sparse channel estimation in FDD massive MIMO systems

Yuan HUANG; Yigang HE; Yuting WU; Tongtong CHENG; Yongbo SUI; Shuguang NING

doi:10.11959/j.issn.1000-436x.2021128

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Deep learning for compressed sensing based sparse channel estimation in FDD massive MIMO systems

Papers | 更新时间：2024-06-05

- Deep learning for compressed sensing based sparse channel estimation in FDD massive MIMO systems
- Journal on Communications Vol. 42, Issue 8, Pages: 61-69(2021)
- 作者机构：
  
  1. 合肥工业大学电气与自动化工程学院，安徽合肥 230009
  2. 武汉大学电气工程学院，湖北武汉 430072
- 作者简介：
- 基金信息：
  
  The National Key Research and Development Program of China(2016YFF0102200);The National Natural Science Foundation of China(51577046);The National Natural Science Foundation of China(51637004)
- DOI：10.11959/j.issn.1000-436x.2021128
  CLC： TN929.5
- Online First：2021-08，
  
  Published：25 August 2021
- 稿件说明：
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Yuan HUANG, Yigang HE, Yuting WU, et al. Deep learning for compressed sensing based sparse channel estimation in FDD massive MIMO systems[J]. Journal on Communications, 2021, 42(8): 61-69.
DOI：

Yuan HUANG, Yigang HE, Yuting WU, et al. Deep learning for compressed sensing based sparse channel estimation in FDD massive MIMO systems[J]. Journal on Communications, 2021, 42(8): 61-69. DOI： 10.11959/j.issn.1000-436x.2021128.

摘要

针对FDD大规模多输入多输出（MIMO）下行链路系统，提出了一种新型的基于深度学习的压缩感知稀疏信道估计算法，即卷积重构网络（ConCSNet）。在不需要稀疏度的情况下，通过数据驱动的方式，利用ConCSNet求解从测量向量y到信号h的逆变换过程，从而解决压缩感知框架下的欠定最优化问题，实现对原始稀疏信道的重构。仿真结果表明，所提算法能更快速、准确地恢复稀疏度未知的大规模MIMO系统的信道状态信息。

Abstract

For FDD massive multi-input multi-output (MIMO) downlink system

a novel deep learning method for compressed sensing based sparse channel estimation was proposed

which was called convolutional compressed sensing network (ConCSNet).In the ConCSNet

the convolutional neural network was utilized to solve the inverse transformation process from measurement vector y to signal h and solve the underdetermined optimization problem through data-driven method without sparsity.Simulation results show that the algorithm can recover the channel state information in massive MIMO Systems with unknown sparsity more quickly and accurately.

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references

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