Phasma: An Automatic Modulation Classification System Based on Random Forest

被引：0
|
作者：
Triantafyllakis, Kostis [1 ,2 ]
Surligas, Manolis [1 ,2 ]
Vardakis, George [1 ,2 ]
Papadakis, Stefanos [1 ]
机构：
[1] Fdn Res & Technol Hellas, Inst Comp Sci, Iraklion, Crete, Greece
[2] Univ Crete, Comp Sci Dept, Iraklion, Crete, Greece
来源：
2017 IEEE INTERNATIONAL SYMPOSIUM ON DYNAMIC SPECTRUM ACCESS NETWORKS (IEEE DYSPAN) | 2017年
关键词：
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
We propose an architecture that incorporates an automatic modulation classification (AMC) mechanism, assisted by Random Forest machine learning (ML) classifiers. Using this architecture we are able to distinguish a variety of digital and analog modulation schemes under various SNR environments.
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页数：3
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