scholarly journals Convolutional neural network and multi‐feature fusion for automatic modulation classification

2019 ◽  
Vol 55 (16) ◽  
pp. 895-897 ◽  
Author(s):  
Hao Wu ◽  
Yaxing Li ◽  
Liang Zhou ◽  
Jin Meng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Feature Fusion ◽  
Modulation Classification ◽  
Automatic Modulation Classification

scholarly journals Automatic Modulation Classification Based on Deep Feature Fusion for High Noise Level and Large Dynamic Input

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2117
Author(s):  
Hui Han ◽  
Zhiyuan Ren ◽  
Lin Li ◽  
Zhigang Zhu
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Noise Level ◽  
Background Noise ◽  
Dynamic Range ◽  
Feature Fusion ◽  
Superior Performance ◽  
Modulation Classification ◽  
High Background ◽  
Automatic Modulation Classification

Automatic modulation classification (AMC) is playing an increasingly important role in spectrum monitoring and cognitive radio. As communication and electronic technologies develop, the electromagnetic environment becomes increasingly complex. The high background noise level and large dynamic input have become the key problems for AMC. This paper proposes a feature fusion scheme based on deep learning, which attempts to fuse features from different domains of the input signal to obtain a more stable and efficient representation of the signal modulation types. We consider the complementarity among features that can be used to suppress the influence of the background noise interference and large dynamic range of the received (intercepted) signals. Specifically, the time-series signals are transformed into the frequency domain by Fast Fourier transform (FFT) and Welch power spectrum analysis, followed by the convolutional neural network (CNN) and stacked auto-encoder (SAE), respectively, for detailed and stable frequency-domain feature representations. Considering the complementary information in the time domain, the instantaneous amplitude (phase) statistics and higher-order cumulants (HOC) are extracted as the statistical features for fusion. Based on the fused features, a probabilistic neural network (PNN) is designed for automatic modulation classification. The simulation results demonstrate the superior performance of the proposed method. It is worth noting that the classification accuracy can reach 99.8% in the case when signal-to-noise ratio (SNR) is 0 dB.

scholarly journals Automatic Modulation Classification Using Compressive Convolutional Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 79636-79643 ◽  
Author(s):  
Sai Huang ◽  
Lu Chai ◽  
Zening Li ◽  
Di Zhang ◽  
Yuanyuan Yao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Modulation Classification ◽  
Automatic Modulation Classification
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