A Kind of Wireless Modulation Recognition Method Based on DenseNet and BLSTM

被引：3

作者：

Xie, Xiaosong ^{[1
]}

Yang, Guangsong ^{[1
]}

Jiang, Mengxi ^{[2
]}

Ye, Qiubo ^{[1
]}

Yang, Chen-Fu ^{[3
,4
]}

机构：

[1] Jimei Univ, Sch Informat Engn, Xiamen 361021, Peoples R China

[2] Xiamen Univ, Sch Informat Sci & Technol, Xiamen 361005, Peoples R China

[3] Natl Univ Kaohsiung, Dept Chem & Mat Engn, Kaohsiung 811, Taiwan

[4] Chaoyang Univ Technol, Dept Aeronaut Engn, Taichung 413, Taiwan

来源：

IEEE ACCESS | 2021年 / 9卷

关键词：

Modulation; Neural networks; Feature extraction; Convolutional neural networks; Logic gates; Biological neural networks; Neurons; Radio modulation recognition; deep learning; densely connected convolutional networks; MAXIMUM-LIKELIHOOD-ESTIMATION; NETWORK;

D O I：

10.1109/ACCESS.2021.3111406

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Deep learning has achieved remarkable results in various fields, such as image recognition and classification. However, in the recognition of radio modulation methods, deep learning for different modulation methods of radio signal recognition results are not satisfactory. In this paper, we propose to use densely connected convolutional networks combined with bidirectional recurrent neural networks to identify the radios of 11 different modulation methods. The final results show that our method is more accurate than the traditional convolution neural network in modulation recognition.

引用

页码：125706 / 125713

页数：8

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