Automatic Modulation Classification Based on Constellation Density Using Deep Learning

被引：101

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

共 11 条

[1] Automatic Modulation Classification Using Moments and Likelihood Maximization [J].

Abu-Romoh, Mohannad ;

Aboutaleb, Ahmed ;

Rezki, Zouheir .

IEEE COMMUNICATIONS LETTERS, 2018, 22 (05) :938-941

[2] Automatic Modulation Classification Using Deep Learning Based on Sparse Autoencoders With Nonnegativity Constraints [J].

Ali, Afan ;

Fan Yangyu .

IEEE SIGNAL PROCESSING LETTERS, 2017, 24 (11) :1626-1630

[3] Automatic Modulation Classification Using Combination of Genetic Programming and KNN [J].

Aslam, Muhammad Waqar ;

Zhu, Zhechen ;

Nandi, Asoke Kumar .

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2012, 11 (08) :2742-2750

[4] A Faster Maximum-Likelihood Modulation Classification in Flat Fading Non-Gaussian Channels [J].

Chen, Wenhao ;

Xie, Zhuochen ;

Ma, Lu ;

Liu, Jie ;

Liang, Xuwen .

IEEE COMMUNICATIONS LETTERS, 2019, 23 (03) :454-457

[5] Survey of automatic modulation classification techniques: classical approaches and new trends [J].

Dobre, O. A. ;

Abdi, A. ;

Bar-Ness, Y. ;

Su, W. .

IET COMMUNICATIONS, 2007, 1 (02) :137-156

[6] Deep Residual Learning for Image Recognition [J].

He, Kaiming ;

Zhang, Xiangyu ;

Ren, Shaoqing ;

Sun, Jian .

2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :770-778

[7] Blind Signal PSK/QAM Recognition Using Clustering Analysis of Constellation Signature in Flat Fading Channel [J].

Jajoo, Gaurav ;

Kumar, Yogesh ;

Yadav, Sandeep Kumar .

IEEE COMMUNICATIONS LETTERS, 2019, 23 (10) :1853-1856

[8] Modulation Classification Based on Signal Constellation Diagrams and Deep Learning [J].

Peng, Shengliang ;

Jiang, Hanyu ;

Wang, Huaxia ;

Alwageed, Hathal ;

Zhou, Yu ;

Sebdani, Marjan Mazrouei ;

Yao, Yu-Dong .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 30 (03) :718-727

[9]

Szegedy C, 2017, AAAI CONF ARTIF INTE, P4278

[10] Fold-based Kolmogorov-Smirnov Modulation Classifier [J].

Wang, Fanggang ;

Dobre, Octavia A. ;

Chan, Chung ;

Zhang, Jingwen .

IEEE SIGNAL PROCESSING LETTERS, 2016, 23 (07) :1003-1007

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