Cumulant based automatic modulation classification of QPSK, OQPSK, 8-PSK and 16-PSK

2016 ◽  
Author(s):  
Dibyajyoti Das ◽  
Prabin Kumar Bora ◽  
Ratnajit Bhattacharjee
Keyword(s):  
Modulation Classification ◽  
Automatic Modulation Classification

Automatic Modulation Classification of Overlapped Sources Using Multiple Cumulants

2017 ◽  
Vol 66 (7) ◽  
pp. 6089-6101 ◽  
Author(s):  
Sai Huang ◽  
Yuanyuan Yao ◽  
Zhiqing Wei ◽  
Zhiyong Feng ◽  
Ping Zhang
Keyword(s):  
Modulation Classification ◽  
Automatic Modulation Classification

scholarly journals Performance Analysis of Automatic Modulation Classification using Time Frequency Transforms under Non-Ideal Channel Conditions

2019 ◽  
Vol 8 (12) ◽  
pp. 1685-1691
Keyword(s):  
Performance Analysis ◽  
Fading Channels ◽  
Classification Accuracy ◽  
Modulation Classification ◽  
Time Frequency ◽  
Automatic Modulation Classification ◽  
Performance Metric ◽  
S Transform ◽  
Channel Conditions

Classification of different analog and digital modulation classes using Time-Frequency Transforms (TFTs) through MST and MFSWT under ideal channel conditions is presented in this paper. It also deals with performance analysis of proposed Modified S- Transform (MST) and Modified Frequency Slice Wavelet Transform (MFSWT) based Automatic Modulation Classification (AMC) methods under different channel conditions such as Gaussian and fading channels. The performance of the proposed TFT based AMC methods under AWGN (with SNR values varied from -10 dB to 20 dB) and fading channels is examined through simulation. Moreover, the performance of the proposed TFT based AMC is compared with that of the existing techniques in terms of performance metric namely classification accuracy which is also discussed in this paper.

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.

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