ConvLSTMAE: A Spatiotemporal Parallel Autoencoders for Automatic Modulation Classification

Automatic modulation classification (AMC) is the key technique in both military and civilian wireless communication. However, the performance is unsatisfactory, even several deep learning-based methods are involved. Targeting its low accuracy at low SNR, high computational cost and label overdepende...

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Bibliographic Details
Published inIEEE communications letters Vol. 26; no. 8; pp. 1804 - 1808
Main Authors Yunhao, Shi, Hua, Xu, Lei, Jiang, Zisen, Qi
Format Journal Article
LanguageEnglish
Published New York IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
autoencoder
Automatic modulation classification
Classification
Computing costs
convolution
Feature extraction
Image reconstruction
Kernel
Logic gates
LSTM
Machine learning
Military communications
Modulation
semi-supervised
Symbols
Training
Wireless communications
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Summary:Automatic modulation classification (AMC) is the key technique in both military and civilian wireless communication. However, the performance is unsatisfactory, even several deep learning-based methods are involved. Targeting its low accuracy at low SNR, high computational cost and label overdependence, we propose a novel AMC framework, where the autoencoder (AE) serves as the backbone and Convolution-AE and LSTM-AE are combined in a parallel way as temporal and spatial feature extractors. The comparisons with serval algorithms on the radioML2016.10a show that our proposed network can achieve higher classification accuracy at low SNR with a low cost. In addition, it suits the semi-supervised scenario since the dependence on labels is loosen.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2022.3179003