Automatic modulation classification in α-stable noise using graph-based generalized second-order cyclic spectrum analysis

2019 ◽  
Vol 37 ◽  
pp. 100854 ◽  
Author(s):  
Xiao Yan ◽  
Guoyu Zhang ◽  
Hsiao-Chun Wu ◽  
Guannan Liu
Keyword(s):  
Spectrum Analysis ◽  
Second Order ◽  
Modulation Classification ◽  
Automatic Modulation Classification ◽  
Stable Noise ◽  
Cyclic Spectrum

Robust Modulation Classification Over $\alpha$-Stable Noise Using Graph-Based Fractional Lower-Order Cyclic Spectrum Analysis

2020 ◽  
Vol 69 (3) ◽  
pp. 2836-2849 ◽  
Author(s):  
Xiao Yan ◽  
Guannan Liu ◽  
Hsiao-Chun Wu ◽  
Guoyu Zhang ◽  
Qian Wang ◽  
...  
Keyword(s):  
Spectrum Analysis ◽  
Lower Order ◽  
Modulation Classification ◽  
Stable Noise ◽  
Cyclic Spectrum

Innovative Robust Modulation Classification Using Graph-Based Cyclic-Spectrum Analysis

2017 ◽  
Vol 21 (1) ◽  
pp. 16-19 ◽  
Author(s):  
Xiao Yan ◽  
Guoyu Feng ◽  
Hsiao-Chun Wu ◽  
Weidong Xiang ◽  
Qian Wang
Keyword(s):  
Spectrum Analysis ◽  
Modulation Classification ◽  
Cyclic Spectrum

scholarly journals Automatic Modulation Classification of Digital Communication Signals Using SVM Based on Hybrid Features, Cyclostationary, and Information Entropy

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 745 ◽  
Author(s):  
Yangjie Wei ◽  
Shiliang Fang ◽  
Xiaoyan Wang
Keyword(s):  
Information Entropy ◽  
Digital Communication ◽  
Classification Performance ◽  
Support Vector ◽  
Modulation Classification ◽  
Hybrid Features ◽  
Communication Signals ◽  
Automatic Modulation Classification ◽  
Cyclic Spectrum

Since digital communication signals are widely used in radio and underwater acoustic systems, the modulation classification of these signals has become increasingly significant in various military and civilian applications. However, due to the adverse channel transmission characteristics and low signal to noise ratio (SNR), the modulation classification of communication signals is extremely challenging. In this paper, a novel method for automatic modulation classification of digital communication signals using a support vector machine (SVM) based on hybrid features, cyclostationary, and information entropy is proposed. In this proposed method, by combining the theory of the cyclostationary and entropy, based on the existing signal features, we propose three other new features to assist the classification of digital communication signals, which are the maximum value of the normalized cyclic spectrum when the cyclic frequency is not zero, the Shannon entropy of the cyclic spectrum, and Renyi entropy of the cyclic spectrum respectively. Because these new features do not require any prior information and have a strong anti-noise ability, they are very suitable for the identification of communication signals. Finally, a one against one SVM is designed as a classifier. Simulation results show that the proposed method outperforms the existing methods in terms of classification performance and noise tolerance.

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.

A Novel Automatic Modulation Classification Method Using Attention Mechanism and Hybrid Parallel Neural Network

2021 ◽  
Vol 11 (3) ◽  
pp. 1327
Author(s):  
Rui Zhang ◽  
Zhendong Yin ◽  
Zhilu Wu ◽  
Siyang Zhou
Keyword(s):  
Neural Network ◽  
Communication Systems ◽  
Parallel Structure ◽  
Modulation Classification ◽  
Automatic Modulation Classification ◽  
Spatial Features ◽  
Temporal Features ◽  
Different Types ◽  
In Series ◽  
Parallel Network

Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract different types of features simultaneously in a proper way. When it comes to the output layer, softmax function is applied for classification to expand the inter-class distance. In this paper, we propose a hybrid parallel network for the AMC problem. Our proposed method designs a hybrid parallel structure which utilizes Convolution Neural Network (CNN) and Gate Rate Unit (GRU) to extract spatial features and temporal features respectively. Instead of superposing these two categories of features directly, three different attention mechanisms are applied to assign weights for different types of features. Finally, a cosine similarity metric named Additive Margin softmax function, which can expand the inter-class distance and compress the intra-class distance simultaneously, is adopted for output. Simulation results demonstrate that the proposed method can achieve remarkable performance on an open access dataset.

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