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Modulation Classification Using Compressed Sensing and Decision Tree–Support Vector Machine in Cognitive Radio System

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 re...

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Autores principales: Sun, Xiaoyong, Su, Shaojing, Zuo, Zhen, Guo, Xiaojun, Tan, Xiaopeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085730/
https://www.ncbi.nlm.nih.gov/pubmed/32155737
http://dx.doi.org/10.3390/s20051438
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author Sun, Xiaoyong
Su, Shaojing
Zuo, Zhen
Guo, Xiaojun
Tan, Xiaopeng
author_facet Sun, Xiaoyong
Su, Shaojing
Zuo, Zhen
Guo, Xiaojun
Tan, Xiaopeng
author_sort Sun, Xiaoyong
collection PubMed
description 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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spelling pubmed-70857302020-03-25 Modulation Classification Using Compressed Sensing and Decision Tree–Support Vector Machine in Cognitive Radio System Sun, Xiaoyong Su, Shaojing Zuo, Zhen Guo, Xiaojun Tan, Xiaopeng Sensors (Basel) Article 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. MDPI 2020-03-06 /pmc/articles/PMC7085730/ /pubmed/32155737 http://dx.doi.org/10.3390/s20051438 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sun, Xiaoyong
Su, Shaojing
Zuo, Zhen
Guo, Xiaojun
Tan, Xiaopeng
Modulation Classification Using Compressed Sensing and Decision Tree–Support Vector Machine in Cognitive Radio System
title Modulation Classification Using Compressed Sensing and Decision Tree–Support Vector Machine in Cognitive Radio System
title_full Modulation Classification Using Compressed Sensing and Decision Tree–Support Vector Machine in Cognitive Radio System
title_fullStr Modulation Classification Using Compressed Sensing and Decision Tree–Support Vector Machine in Cognitive Radio System
title_full_unstemmed Modulation Classification Using Compressed Sensing and Decision Tree–Support Vector Machine in Cognitive Radio System
title_short Modulation Classification Using Compressed Sensing and Decision Tree–Support Vector Machine in Cognitive Radio System
title_sort modulation classification using compressed sensing and decision tree–support vector machine in cognitive radio system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085730/
https://www.ncbi.nlm.nih.gov/pubmed/32155737
http://dx.doi.org/10.3390/s20051438
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