Automatic Modulation Classification Based on Constellation Density Using Deep Learning

被引：97

作者：

Kumar, Yogesh ^{[1
]}

Sheoran, Manu ^{[1
]}

Jajoo, Gaurav ^{[1
]}

Yadav, Sandeep Kumar ^{[1
]}

机构：

[1] IIT Jodhpur, Dept Elect Engn, Jodhpur 342037, Rajasthan, India

来源：

IEEE COMMUNICATIONS LETTERS | 2020年 / 24卷 / 06期

关键词：

Feature extraction; Color; Phase shift keying; Signal to noise ratio; Training; Quadrature amplitude modulation; Modulation classification; deep learning; constellation; color image;

D O I：

10.1109/LCOMM.2020.2980840

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

Deep learning (DL) is a newly addressed area of research in the field of modulation classification. In this letter, a constellation density matrix (CDM) based modulation classification algorithm is proposed to identify different orders of ASK, PSK, and QAM. CDM is formed through local density distribution of the signal's constellation generated using LabVIEW for a wide range of SNR. Two DL models, ResNet-50 and Inception ResNet V2 are trained through color images formed by filtering the CDM. Classification accuracy achieved demonstrates better performance compared to many existing classifiers in the literature.

引用

页码：1275 / 1278

页数：4

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