Modulation classification based on denoising autoencoder and convolutional neural network with GNU radio

Modulation classification based on denoising autoencoder and convolutional neural network with GNU radio

In this article, a machine learning method to classify signal with Gaussian noise based on denoising auto encoder (DAE) and convolutional neural network (CNN) is proposed. We combine denoising autoencoder's denoising ability with CNN's feature extraction capability. First, a six-layer neur...

Full description

Bibliographic Details
Main Authors: Jun Wang, Wenfeng Wang, Feixiang Luo, Shaoming Wei
Format: Article
Language:English
Published: Wiley 2019-07-01
Series:The Journal of Engineering
Subjects:
signal classification
modulation
feature extraction
Gaussian noise
signal denoising
convolutional neural nets
software radio
machine learning method
convolutional neural network
six-layer neural network
CNN layers
denoising autoencoder
modulation classification
CNN feature extraction
signal-to-noise ratio
GNU radio
noise figure 5.0 dB
noise figure 18.0 dB
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0203

Internet

https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0203

Similar Items