Modulation Recognition for Incomplete signals through Dictionary Learning

被引：0

作者：

Lu, Guangcheng ^{[1
]}

Zhang, Kezhong ^{[1
]}

Huang, Sai ^{[1
]}

Zhang, Yifan ^{[1
]}

Feng, Zhiyong ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, Wireless Technol Innovat Inst WTI, Beijing 100876, Peoples R China

来源：

2017 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC) | 2017年

基金：

中国国家自然科学基金;

关键词：

Incomplete signals; modulation recognition; dictionary learning; CLASSIFICATION;

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：

The automatic recognition of modulation type for a detected signal is a significant task in wireless communication, which is the intermediate step between signal detection and demodulation. There are two general methods adopted in modulation recognition, i.e., likelihood-based (LB) method and feature-based (FB) method. Both LB and FB approach do not perform well when the signals are incomplete and received from a very limited number of observations. Therefore, we adopt a method based on dictionary learning to identify the modulation type of incomplete signals. The orthogonal matching pursuit (OMP) method is used to obtain the sparse representation and the sequential generalization of K-means (SGK) method is used to update the dictionary set. The experimental result shows that the recognition accuracy of our method is much higher, compared with the method based on higher-order cumulant.

引用

页数：6

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