Identifying modulation formats through 2D Stokes planes with deep neural networks

被引：41

作者：

Zhang, Wenbo ^{[1
]}

Zhu, Dingcheng ^{[1
]}

He, Zihang ^{[1
]}

Zhang, Nannan ^{[2
]}

Zhang, Xiaoguang ^{[2
]}

Zhang, Hu ^{[3
]}

Li, Yong ^{[4
]}

机构：

[1] Beijing Univ Posts & Telecommun, Sch Sci, Beijing, Peoples R China

[2] Beijing Univ Posts & Telecommun, State Key Lab Informat Photon & Opt Commun, Beijing, Peoples R China

[3] Beijing Univ Posts & Telecommun, Sch Ethn Minor Educ, Beijing, Peoples R China

[4] Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing Key Lab Work Safety & Intelligent Monitor, Beijing, Peoples R China

来源：

OPTICS EXPRESS | 2018年 / 26卷 / 18期

基金：

中国国家自然科学基金;

关键词：

IDENTIFICATION;

D O I：

10.1364/OE.26.023507

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

A lightweight convolutional (deep) neural networks (CNNs) based modulation format identification (MFI) scheme in 2D Stokes planes for polarization domain multiplexing (PDM) fiber communication system is proposed and demonstrated. Influences of the learning rate of CNN is discussed. Experimental verifications are performed for the PDM system at a symbol rate of 28GBaud. Six modulation formats are identified with a trained CNN from images of received signals. They are PDM-BPSK, PDM-QPSK, PDM-8PSK, PDM-16QAM, PDM-32QAM, and PDM-64QAM. By taking advantage of computer vision, the results show that the proposed scheme can significantly improve the identification performance over the existing techniques. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

引用

页码：23507 / 23517

页数：11

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