基于深度学习的压缩感知FDD大规模MIMO系统稀疏信道估计算法

黄源; 何怡刚; 吴裕庭; 程彤彤; 隋永波; 宁暑光

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

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基于深度学习的压缩感知FDD大规模MIMO系统稀疏信道估计算法

学术论文 | 更新时间：2024-06-05

- 基于深度学习的压缩感知FDD大规模MIMO系统稀疏信道估计算法
- Deep learning for compressed sensing based sparse channel estimation in FDD massive MIMO systems
- 通信学报 2021年42卷第8期页码：61-69
- 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：
  
  [ "黄源（1993- ），男，湖北黄石人，合肥工业大学博士生，主要研究方向为大规模MIMO无线信道估计和压缩感知技术" ]
  [ "何怡刚（1966- ），男，湖南邵阳人，博士，合肥工业大学教授、博士生导师，主要研究方向为模拟和混合集成电路设计、测试与故障诊断、智能电网技术、射频识别技术、虚拟仪器和智能信号处理" ]
  [ "吴裕庭（1992- ），男，安徽铜陵人，合肥工业大学博士生，主要研究方向为无线信道建模" ]
  [ "程彤彤（1993- ），男，安徽淮南人，合肥工业大学博士生，主要研究方向为无线信道预编码技术" ]
  [ "隋永波（1990- ），男，山东潍坊人，合肥工业大学博士生，主要研究方向为无线信道预测技术" ]
  [ "宁暑光（1991- ），男，安徽阜阳人，合肥工业大学博士生，主要研究方向为电力设备故障诊断与定位" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2016YFF0102200);国家自然科学基金资助项目(51577046);国家自然科学基金资助项目(51637004)
- DOI：10.11959/j.issn.1000-436x.2021128
  中图分类号： TN929.5
- 网络首发：2021-08，
  
  纸质出版：2021-08-25
- 稿件说明：
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黄源, 何怡刚, 吴裕庭, 等. 基于深度学习的压缩感知FDD大规模MIMO系统稀疏信道估计算法[J]. 通信学报, 2021,42(8):61-69.

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.
黄源, 何怡刚, 吴裕庭, 等. 基于深度学习的压缩感知FDD大规模MIMO系统稀疏信道估计算法[J]. 通信学报, 2021,42(8):61-69. DOI： 10.11959/j.issn.1000-436x.2021128.

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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