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| Content Provider | IET Digital Library |
|---|---|
| Author | Wang, Jun Wang, Wenfeng Luo, Feixiang Wei, Shaoming |
| Abstract | 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 neural network is built, including three CNN layers. Then a dataset containing noiseless signal of 11 modulation is generated. In the simulation, we apply this dataset to train neural network and achieve an accuracy of 94%, which is much higher than performance with noisy signal, meaning that noise can greatly influence the accuracy of neural network. Next, we build a denoising autoencoder and train it with signal of 5 dB signal-to-noise ratio (SNR). Compared with neural network without denoising autoencoder, adding a denoising autoencoder can achieve an accuracy of 84% at signal of 18 dB SNR, improved by 58%. |
| Starting Page | 6188 |
| Ending Page | 6191 |
| Page Count | 4 |
| Volume Number | 2019 |
| e-ISSN | 20513305 |
| Issue Number | Issue 19, Oct (2019) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2019/19 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0203 |
| Journal | The Journal of Engineering |
| Publisher | The Institution of Engineering and Technology |
| Publisher Date | 2019-05-30 |
| Access Restriction | Open |
| Rights License | Creative Commons Attribution-Non Commercial-No Derivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/) |
| Subject Keyword | CNN Feature Extraction CNN Layer Convolutional Neural Nets Convolutional Neural Network Denoising Autoencoder Digital Signal Processing Feature Extraction Gaussian Noise GNU Radio Machine Learning Method Modulation Modulation Classification Neural Computing Technique Noise Figure 18.0 DB Noise Figure 5.0 DB Signal Classification Signal Denoising Signal Processing And Detection Signal to Noise Ratio Six-layer Neural Network Software Radio Statistics |
| Content Type | Text |
| Resource Type | Article |
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