基于Res-DNN的端到端MIMO系统信号检测算法

doi:10.11959/j.issn.2096-3750.2022.00256

物联网学报 ›› 2022, Vol. 6 ›› Issue (1): 65-72.doi: 10.11959/j.issn.2096-3750.2022.00256

基于Res-DNN的端到端MIMO系统信号检测算法

李国权¹^,², 徐永海¹^,², 林金朝¹^,², 黄正文³

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

修回日期:2022-01-03 出版日期:2022-03-30 发布日期:2022-03-01
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
基金资助:
国家重点研发计划(2019YFC1511300);重庆市自然科学基金资助项目(cstc2019jcyj-msxmX0666);重庆市自然科学基金资助项目(cstc2019jcyj-xfkxX0002)

Res-DNN based signal detection algorithm for end-to-end MIMO systems

Guoquan LI¹^,², Yonghai XU¹^,², Jinzhao LIN¹^,², Zhengwen HUANG³

¹ School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
² Chongqing Key Lab of Photoelectronic Information Sensing and Transmitting Technology, Chongqing 400065, China
³ Department of Electronic and Computer Engineering, Brunel University London, London UB8 3PH, UK

Revised:2022-01-03 Online:2022-03-30 Published:2022-03-01
Supported by:
The National Key Research and Development Program of China(2019YFC1511300);The Natural Science Foundation of Chongqing(cstc2019jcyj-msxmX0666);The Natural Science Foundation of Chongqing(cstc2019jcyj-xfkxX0002)

摘要/Abstract

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。

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

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.

Key words: deep learning, MIMO system, signal detection, Res-DNN, end-to-end

中图分类号:

TN92

李国权, 徐永海, 林金朝, 黄正文. 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, Zhengwen HUANG. Res-DNN based signal detection algorithm for end-to-end MIMO systems[J]. Chinese Journal on Internet of Things, 2022, 6(1): 65-72.

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类型	参数配置
天线数目	8×8/16×16
信道类型	瑞利信道
调制方式	BPSK/QPSK
训练数据的信噪比	15 dB
神经网络层数	11层
训练次数	20 000次
神经元数目	128个
激活函数	ReLU
初始学习率	0.000 3
损失函数	二次代价函数
优化器	Adam

检测算法	计算时间/s
ZF检测	0.000 025 1
MMSE检测	0.000 049 8
ML检测	0.525 025 8
DetNet检测	0.000 281 7
所提方案	0.000 451 4

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

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