Modulation classification based on the collaboration of dual-channel CNN-LSTM and residual network

doi:10.19682/j.cnki.1005-8885.2022.2012

The Journal of China Universities of Posts and Telecommunications ›› 2022, Vol. 29 ›› Issue (1): 113-124.doi: 10.19682/j.cnki.1005-8885.2022.2012

• Networks • Previous Articles

Modulation classification based on the collaboration of dual-channel CNN-LSTM and residual network

Li Hui, Li Shanshan, Zou Borong, Chen Yannan

School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China

Received:2021-04-09 Revised:2021-07-06 Accepted:2021-09-27 Online:2022-02-26 Published:2022-02-28
Contact: Corresponding author: Zou Borong E-mail:wdzbr296@hpu.edu.cn
Supported by:
This work was supported by the Project of Henan Science and Technology Research (222102210247).

Abstract

Abstract: Deep learning has recently been progressively introduced into the field of modulation classification due to its wide application in image, vision, and other areas. Modulation classification is not only the priority of cognitive radio and spectrum sensing, but also the link during signal demodulation. Combining the advantages of convolutional neural network (CNN), long short-term memory (LSTM), and residual network (ResNet), a modulation classification method based on dual-channel CNN-LSTM and ResNet is proposed to automatically classify the modulation signal more accurately. Specifically, CNN and LSTM are initially used to form a dual-channel structure to effectively explore the spatial and temporal features of the original complex signal. It solves the problem of only focusing on temporal or spatial aspects, and increases the diversity of features. Secondly, the features extracted from CNN and LSTM are fused, making the extracted features richer and conducive to signal classification. In addition, a convolutional layer is added within the residual unit to deepen the network depth. As a result, more representative features are extracted, improving the classification performance. Finally, simulation results on the radio machine learning (RadioML) 2018.01A dataset signify that the network's classification performance is superior to many classifiers in the literature.

Key words: convolutional neural network, deep neural network, long short-term memory, modulation classification, residual network

CLC Number:

TN911.7

Li Hui, Li Shanshan, Zou Borong, Chen Yannan. Modulation classification based on the collaboration of dual-channel CNN-LSTM and residual network[J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(1): 113-124.

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Modulation classification based on the collaboration of dual-channel CNN-LSTM and residual network

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