Blind recognition algorithm for multi-STBC based on deep learning

被引：1
|
作者：
Yu K. [1 ]
Zhang L. [1 ]
Yan W. [1 ]
Jin K. [2 ]
机构：
[1] Institute of Information Fusion, Naval Aviation University, Yantai
[2] School of Basis of Aviation, Naval Aviation University, Yantai
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2021年 / 43卷 / 04期
关键词：
Autocorrelation function; Blind signal recognition; Convolutional neural network; Data preprocessing; Space-time block code;
D O I：
10.12305/j.issn.1001-506X.2021.04.29
中图分类号：
学科分类号：
摘要：
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. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：1110 / 1118
页数：8
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