Robust Automatic Modulation Classification in Low Signal to Noise Ratio

In a non-cooperative communication environment, automatic modulation classification (AMC) is an essential technology for analyzing signals and classifying different kinds of signal modulation before they are demodulated. Deep learning (DL)-based AMC has been proposed as an efficient method of achiev...

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Bibliographic Details
Published in:IEEE access 2023, Vol.11, p.7860-7872
Main Authors: An, To Truong, Lee, Byung Moo
Format: Article
Language:English
Subjects:
Artificial neural networks
autoencoder denoiser
Automatic modulation classification
Classification
Classification algorithms
Computer networks
convolutional neural network
Convolutional neural networks
Deep learning
denoise signal
Feature extraction
Machine learning
Modulation
Neural networks
Noise reduction
Signal classification
Signal processing algorithms
Signal to noise ratio
Technology assessment
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Summary:In a non-cooperative communication environment, automatic modulation classification (AMC) is an essential technology for analyzing signals and classifying different kinds of signal modulation before they are demodulated. Deep learning (DL)-based AMC has been proposed as an efficient method of achieving high classification performance. However, most current DL-AMC methods have limited generalization capabilities under varying noise conditions, especially at low signal-to-noise ratios (SNRs). Therefore, these methods can not be directly applied to practical systems. In this paper, we propose a threshold autoencoder denoiser convolutional neural network (TADCNN), which consists of a threshold autoencoder denoiser (TAD) and a convolutional neural network (CNN). TADs reduce noise power and clean input signals, which are then passed on to CNN for classification. The TAD network generally consists of three components: the batch normalization layer, the autoencoder, and the threshold denoise. The threshold denoise component uses an auto-learning threshold sub-network to compute thresholds automatically. According to experiments, AMC with TAD improved classification accuracy by 70% at low SNR compared with a model without a denoiser. Additionally, our model achieves an average accuracy of 66.64% on the RML2016.10A dataset, which is 6% to 18% higher than the current AMC model.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3238995