基于深度学习的多STBC盲识别算法

doi:10.12305/j.issn.1001-506X.2021.04.29

系统工程与电子技术 ›› 2021, Vol. 43 ›› Issue (4): 1110-1118.doi: 10.12305/j.issn.1001-506X.2021.04.29

基于深度学习的多STBC盲识别算法

于柯远¹(), 张立民¹(), 闫文君^1,*(), 金堃²()

1. 海军航空大学信息融合研究所, 山东烟台 264001
2. 海军航空大学航空基础学院, 山东烟台 264001

收稿日期:2020-05-08 出版日期:2021-03-25 发布日期:2021-03-31
通讯作者: 闫文君 E-mail:gfsskfqp@163.com;iamzlm@163.com;wj_yan@foxmail.com;jinkunhg@163.com
作者简介:于柯远(1992-), 男, 博士研究生, 主要研究方向为信号处理新技术。E-mail: gfsskfqp@163.com|张立民(1966-), 男, 教授, 博士, 主要研究方向为电子仿真技术和卫星信号处理。E-mail: iamzlm@163.com|闫文君(1986-), 男, 副教授, 博士, 主要研究方向为通信信号处理和计算机仿真。E-mail: wj_yan@foxmail.com|金堃(1993-), 女, 硕士, 主要研究方向为通信信号处理和电子技术仿真。E-mail: jinkunhg@163.com
基金资助:
国家自然科学基金重大研究计划(91538201);泰山学者工程专项(201511020)

Blind recognition algorithm for multi-STBC based on deep learning

Keyuan YU¹(), Limin ZHANG¹(), Wenjun YAN^1,*(), Kun JIN²()

1. Institute of Information Fusion, Naval Aviation University, Yantai 264001, China
2. School of Basis of Aviation, Naval Aviation University, Yantai 264001, China

Received:2020-05-08 Online:2021-03-25 Published:2021-03-31
Contact: Wenjun YAN E-mail:gfsskfqp@163.com;iamzlm@163.com;wj_yan@foxmail.com;jinkunhg@163.com

摘要/Abstract

摘要：

针对空时分组码(space-time block code, STBC)识别中多种编码类型难区分的问题, 提出了一种基于卷积神经网络的STBC盲识别算法。该算法首先将接收信号采用自相关函数的频域预处理, 输入到卷积神经网络中对信号特征进行提取, 全连接层对特征进行映射, 实现对6种STBC类型的识别。仿真实验结果表明, 在无信道和噪声等先验信息的条件下, 所提算法能够有效区分3种相似度高的STBC3码, 且将STBC可识别的编码类型由目前的4种扩充到6种, 识别准确率能达到96%。该方法的复杂度较低, 不需要利用大量样本数据, 实时性高, 具有较好的工程应用价值。

关键词: 信号盲识别, 空时分组码, 卷积神经网络, 数据预处理, 自相关函数

Abstract:

To solve the problem that different coding types are difficult to distinguish in space-time block code (STBC) recognition, a blind algorithm is proposed for STBC recognition based on convolutional neural network. In this algorithm, the received signal is preprocessed in frequency domain by autocorrelation function, input into the convolutional neural network to extract the signal features, and the features are mapped at the full connection layer to realize the recognition of six STBC types. Simulation experiment results show that the proposed algorithm can effectively distinguish three STBC3 codes of high similarity in the absence of channel and noise under the condition of a priori information, and the recognizable code type of STBC can be expanded from the current four to six, identification accuracy can reach 96%. The complexity of this method is low, and does not need to use a large number of sample data, Which has high real-time performance and good engineering application value.

Key words: blind signal recognition, space-time block code, convolutional neural network, data preprocessing, autocorrelation function

中图分类号:

TN911.7

于柯远, 张立民, 闫文君, 金堃. 基于深度学习的多STBC盲识别算法[J]. 系统工程与电子技术, 2021, 43(4): 1110-1118.

Keyuan YU, Limin ZHANG, Wenjun YAN, Kun JIN. Blind recognition algorithm for multi-STBC based on deep learning[J]. Systems Engineering and Electronics, 2021, 43(4): 1110-1118.

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网络层	输入信号	网络层参数	激活函数
卷积层	2×128	1×4卷积核256个特征映射Dropout 50%	ReLU
卷积层	1×4×256	2×3卷积核80个特征映射Dropout 50%	ReLU
全连接层	256	256个神经元Dropout 50%	ReLU
全连接层	6	6个神经元	SoftMax

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基于深度学习的多STBC盲识别算法

Blind recognition algorithm for multi-STBC based on deep learning

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