基于Res-DNN的端到端MIMO系统信号检测算法

李国权; 徐永海; 林金朝; 黄正文

doi:10.11959/j.issn.2096-3750.2022.00256

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基于Res-DNN的端到端MIMO系统信号检测算法

理论与技术 | 更新时间：2024-06-05

- 基于Res-DNN的端到端MIMO系统信号检测算法
- Res-DNN based signal detection algorithm for end-to-end MIMO systems
- 物联网学报 2022年6卷第1期页码：65-72
- 作者机构：
  
  1. 重庆邮电大学通信与信息工程学院，重庆 400065
  2. 光电信息感测与传输技术重庆市重点实验室，重庆 400065
  3. 伦敦布鲁内尔大学电子与计算机工程系，英国伦敦 UB8 3PH
- 作者简介：
  
  [ "李国权（1980− ），男，博士，重庆邮电大学通信与信息工程学院教授，主要研究方向为MIMO无线通信传输技术、异构无线网络传输技术、机器学习等" ]
  [ "徐永海（1995− ），男，重庆邮电大学通信与信息工程学院硕士生，主要研究方向为MIMO无线通信传输技术、机器学习等" ]
  [ "林金朝（1966− ），男，博士，重庆邮电大学通信与信息工程学院教授，主要研究方向为无线通信传输技术、BAN与信息处理技术等" ]
  [ "黄正文（1981− ），男，博士，伦敦布鲁内尔大学讲师、高级研究员，主要研究方向为人工智能、复杂系统优化、数据分析等" ]
- 基金信息：
  
  国家重点研发计划;The National Key Research and Development Program of China(2019YFC1511300);重庆市自然科学基金资助项目;The Natural Science Foundation of Chongqing(cstc2019jcyj-msxmX0666);重庆市自然科学基金面上项目;Natural Science Foundation of Chongqing(cstc2019jcyj-xfkxX0002)
- DOI：10.11959/j.issn.2096-3750.2022.00256
  中图分类号： TN92
- 纸质出版日期：2022-03-30，
  
  网络出版日期：2022-03，
- 稿件说明：
移动端阅览
李国权, 徐永海, 林金朝, 等. 基于Res-DNN的端到端MIMO系统信号检测算法[J]. 物联网学报, 2022,6(1):65-72.

GUOQUAN LI, YONGHAI XU, JINZHAO LIN, et al. Res-DNN based signal detection algorithm for end-to-end MIMO systems. [J]. Chinese journal on internet of things, 2022, 6(1): 65-72.
李国权, 徐永海, 林金朝, 等. 基于Res-DNN的端到端MIMO系统信号检测算法[J]. 物联网学报, 2022,6(1):65-72. DOI： 10.11959/j.issn.2096-3750.2022.00256.

GUOQUAN LI, YONGHAI XU, JINZHAO LIN, et al. Res-DNN based signal detection algorithm for end-to-end MIMO systems. [J]. Chinese journal on internet of things, 2022, 6(1): 65-72. DOI： 10.11959/j.issn.2096-3750.2022.00256.

摘要

深度学习可通过提取无线通信数据的内在特征提升信号检测效果。针对MIMO系统信号检测存在的性能与复杂度的折中问题，提出一种基于深度学习的端到端MIMO系统信号检测方案。基于残差深度神经网络的编码器和解码器分别替代无线通信系统的发送端和接收端，将它们看作一个整体通过端到端的方式进行训练。编码器首先对输入数据进行特征提取，进而建立通信模型并传入迫零检测器进行初步检测，最终通过解码器重构得出检测信号。仿真结果表明，所提检测方案优于同类型算法，并且在牺牲一定时间复杂度的情况下，检测性能明显优于MMSE检测算法。

Abstract

Deep learning can improve the effect of signal detection by extracting the inherent characteristics of wireless communication data.To solve the tradeoff between the performance and complexity of MIMO system signal detection

an end-to-end MIMO system signal detection scheme based on deep learning was proposed.The encoder and the decoder based on residual deep neural network replace the transmitter and the receiver of the wireless communication system respectively

and they were trained in an end-to-end manner as a whole.Firstly

the features of the input data were extracted by encoder

then the communication model was established and was sent to the zero forcing detector for preliminary detection.Finally

the detection signal was reconstructed through the decoder.Simulation results show that the proposed detection scheme is superior to the same type of algorithm

and the detection performance is significantly better than that of the MMSE detection algorithm at the expense of a certain time complexity.

关键词

深度学习MIMO系统信号检测残差深度神经网络端到端

Keywords

deep learningMIMO systemsignal detectionRes-DNNend-to-end

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