基于卷积神经网络的串行空时分组码盲识别算法

doi:10.12305/j.issn.1001-506X.2021.11.37

Abstract

Abstract:

Aiming at the problem of the blind recognition of space-time block code (STBC) in multiple input single output (MISO) system, a recognition method of serial STBC recognition method based on convolutional neural network (CNN) is proposed. Firstly, the basic CNN-B network framework is proposed for STBC recognition. Then, on the basis of STBC correlation analysis, a correlation-based CNN-BC network model is designed for the problem of special multiplexing and Alamouti signal aliasing. Finally, the STBC dataset is input into the network model to complete the training and recognition test of the network. The simulation results show that compared with the traditional algorithm based on feature extraction, this method extends the recognized STBC to 6 kinds, and has higher recognition accuracy under low signal to noise ratio, besides, the recognition process can be controlled at the level of microsecond, which has higher engineering application value.

Key words: multiple input single output (MISO), space-time block code (STBC), correlation analysis, convolutional neural network (CNN), blind recognition

CLC Number:

TN911.7

Yuyuan ZHNAG, Wenjun YAN, Limin ZHANG, Yuan ZHANG. Blind recognition algorithm of serial space-time block code based on convolutional neural network[J]. Systems Engineering and Electronics, 2021, 43(11): 3360-3370.

Figures/Tables 16

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References 30

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网络层	输入大小	网络参数
C1	2×128	256×(1×3)
C2	256×(2×126)	80×(2×3)
D1	80×(1×124)	256
D2	256	6

STBC类型	标签	信号数	样本数(2×128)
SM	0	21 000×128	21 000
AL	1	21 000×128	21 000
STBC3-1	2	21 000×128	21 000
STBC3-2	3	21 000×128	21 000
STBC3-3	4	21 000×128	21 000
STBC4	5	21 000×128	21 000

网络	空间复杂度	时间复杂度
网络	参数量	训练总时间/s	识别耗时/μs
CNN-B	2 665 302	78	11.4
CNN-BC	2 636 966	68	9.4
FDSCF+CNN	2 645 078	137	11.9
CNN-LSTM	1 059 522	1 915	591.4

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Blind recognition algorithm of serial space-time block code based on convolutional neural network

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