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

摘要/Abstract

摘要：

针对多输入单输出(multiple input single output, MISO)系统中的空时分组码(space-time block code, STBC)盲识别问题, 提出了一种基于卷积神经网络(convolutional neural network, CNN)的串行STBC识别方法。首先, 结合STBC识别问题提出了基本CNN (CNN basic, CNN-B)框架; 然后在分析STBC相关性的基础上, 针对空间复用和Alamouti信号混叠问题, 设计了基于相关性的CNN (CNN based on correlation, CNN-BC)模型; 最后将STBC数据集输入到网络模型中, 完成网络的训练和识别测试。仿真结果表明, 相比于基于特征提取的传统算法, 该方法将可识别的STBC扩展到了6种, 并且在低信噪比下的识别准确率更高, 识别过程可控制在微秒级别, 具有较高的工程应用价值。

关键词: 多输入单输出, 空时分组码, 相关性分析, 卷积神经网络, 盲识别

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

中图分类号:

TN911.7

张聿远, 闫文君, 张立民, 张媛. 基于卷积神经网络的串行空时分组码盲识别算法[J]. 系统工程与电子技术, 2021, 43(11): 3360-3370.

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.

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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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