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

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

Cyclic Spectrum Analysis of OFDM Signals

2011 ◽  
Vol 30 (1) ◽  
pp. 134-138 ◽  
Author(s):  
Qi-shan Huang ◽  
Qi-cong Peng ◽  
You-rong Lu ◽  
Meng Han
Keyword(s):  
Spectrum Analysis ◽  
Ofdm Signals ◽  
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 Modulation Classification Using Compressed Sensing and Decision Tree–Support Vector Machine in Cognitive Radio System

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1438 ◽  
Author(s):  
Xiaoyong Sun ◽  
Shaojing Su ◽  
Zhen Zuo ◽  
Xiaojun Guo ◽  
Xiaopeng Tan
Keyword(s):  
Support Vector Machine ◽  
Cognitive Radio ◽  
Compressed Sensing ◽  
Decision Tree ◽  
Identification Accuracy ◽  
Support Vector ◽  
Modulation Classification ◽  
Radio System ◽  
Feature Parameters ◽  
Cyclic Spectrum

In this paper, a blind modulation classification method based on compressed sensing using a high-order cumulant and cyclic spectrum combined with the decision tree–support vector machine classifier is proposed to solve the problem of low identification accuracy under single-feature parameters and reduce the performance requirements of the sampling system. Through calculating the fourth-order, eighth-order cumulant and cyclic spectrum feature parameters by breaking through the traditional Nyquist sampling law in the compressed sensing framework, six different cognitive radio signals are effectively classified. Moreover, the influences of symbol length and compression ratio on the classification accuracy are simulated and the classification performance is improved, which achieves the purpose of identifying more signals when fewer feature parameters are used. The results indicate that accurate and effective modulation classification can be achieved, which provides the theoretical basis and technical accumulation for the field of optical-fiber signal detection.

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