基于深度学习的多STBC盲识别算法

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

Abstract

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

CLC Number:

TN911.7

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.

Figures/Tables 11

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References 27

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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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Blind recognition algorithm for multi-STBC based on deep learning

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