Modulation Signal Denoising Based on Auto-encoder

This paper proposed a denoising method for modulated signals based on the autoencoder. The auto-encoder is a cascade structure, which is composed of multiple convolution layers and multiple pooling layers. It is mainly divided into a feature encoder and a generation decoder. We use the features of t...

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Published in:IEEE International Symposium on Broadband Multimedia Systems and Broadcasting pp. 1 - 5
Main Authors: Mo, Zunyin, Li, Hongli, Wang, Jiao, Huang, Hao, Li, Jianqing
Format: Conference Proceeding
Language:English
Published: IEEE 04.08.2021
Subjects:
Advanced signal processing for transmission
Complexity theory
convolution neural network
denoising autoencoder
Feature extraction
Machine learning for communications
Modulation
Neural networks
Noise reduction
Signal processing algorithms
Stability analysis
ISSN:2155-5052
Online Access:Get full text
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Abstract This paper proposed a denoising method for modulated signals based on the autoencoder. The auto-encoder is a cascade structure, which is composed of multiple convolution layers and multiple pooling layers. It is mainly divided into a feature encoder and a generation decoder. We use the features of the modulated signal with noise as the input of the auto-encoder and the features of the clean signal as the label. At the same time, back-propagation algorithm and gradient descent method are used to optimize and update the parameters in the auto-encoder model to minimize the reconstruction error, so as to realize the denoising function of the modulated signal. For a variety of modulation types, this method can improve the modulation signal about 3-9 dB in different SNR environment. The denoising model can generate high-level features of different modulation signals without any artificial feature extraction and prior knowledge and has strong feature representation ability. It has the advantages of strong versatility, low complexity, good denoising effect and good stability.
AbstractList This paper proposed a denoising method for modulated signals based on the autoencoder. The auto-encoder is a cascade structure, which is composed of multiple convolution layers and multiple pooling layers. It is mainly divided into a feature encoder and a generation decoder. We use the features of the modulated signal with noise as the input of the auto-encoder and the features of the clean signal as the label. At the same time, back-propagation algorithm and gradient descent method are used to optimize and update the parameters in the auto-encoder model to minimize the reconstruction error, so as to realize the denoising function of the modulated signal. For a variety of modulation types, this method can improve the modulation signal about 3-9 dB in different SNR environment. The denoising model can generate high-level features of different modulation signals without any artificial feature extraction and prior knowledge and has strong feature representation ability. It has the advantages of strong versatility, low complexity, good denoising effect and good stability.
Author Li, Hongli
Li, Jianqing
Huang, Hao
Mo, Zunyin
Wang, Jiao
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  givenname: Hongli
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  email: lijq@uestc.edu.cn
  organization: School of Electronic Science and Engineering, University of Electronic Science and Technology of China,Chengdu,China
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Snippet This paper proposed a denoising method for modulated signals based on the autoencoder. The auto-encoder is a cascade structure, which is composed of multiple...
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SubjectTerms Advanced signal processing for transmission
Complexity theory
convolution neural network
denoising autoencoder
Feature extraction
Machine learning for communications
Modulation
Neural networks
Noise reduction
Signal processing algorithms
Stability analysis
Title Modulation Signal Denoising Based on Auto-encoder
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