Automatic Modulation Classification Using Support Vector Machines and Error Correcting Output Codes

被引：0

作者：

Li, Jie ^{[1
]}

Meng, Qingda ^{[2
]}

Zhang, Ge ^{[1
]}

Sun, Yang ^{[1
]}

Qiu, Lede ^{[2
]}

Ma, Wei ^{[1
]}

机构：

[1] China Acad Space Technol Xian, Xian 710000, Shaanxi, Peoples R China

[2] CAST, Inst Telecommun Satellite, Beijing 100094, Peoples R China

来源：

PROCEEDINGS OF 2017 IEEE 2ND INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC) | 2017年

关键词：

Modulation Classification; Support Vector Machines; Error Correcting Output Codes;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A new approach, which because of support vector machines (SVMs) and error correcting output codes (ECOC), is proposed for automatic digital modulation classification (MC). Combining statistics and spectral feature sets, SVM classification and ECOC techniques are used to improve the generalization ability of classifiers. In contrast to the popular artificial neural networks (ANN) approach, this method avoids the problem of local optimum and requires less training data. Simulation results show that the proposed method is more efficient than other methods in low signal-to-noise ratio.

引用

页码：60 / 63

页数：4

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