S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Automatic Modulation Recognition Method Based on Hybrid Model of Convolutional Neural Networks and Gated Recurrent Units

Xinyu Hao, Yu Luo, Qiubo Ye, Qi He, Chin-Cheng Chen, and Guangsong Yang

(Received June 9, 2021; Accepted September 16, 2021)

Keywords: convolutional neural networks, global average pooling, gate recurrent units, automatic modulation recognition

With the application of various wireless communication technologies, the electromagnetic environment has become more complex, and the recognition of signal modulation has become increasingly difficult. In this paper, a hybrid model based on deep learning, which aims to quickly classify received modulated signals and help to plan spectrum resources, is proposed. The model is designed by considering the characteristics of convolutional neural networks (CNNs), global average pooling (GAP), gate recurrent units (GRUs), and other structures. Firstly, signal spatial features are extracted by convolution using a CNN, the dimension of the high-dimensional feature map is reduced by GAP, then the signal temporal correlation is extracted using GRUs. Finally, modulation modes are classified in the softmax layer to classify and recognize the modulation modes of the received signal. Experimental results show that the average recognition rate of the model was 60.64% and the maximum recognition rate was 90%. The proposed method not only improves the recognition performance, but also enhances the interpretability of the network.

Corresponding author: Chin-Cheng Chen, Guangsong Yang




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