Nonparameter Nonlinear Phase Noise Mitigation by Using M-ary Support Vector Machine for Coherent Optical Systems

Author

Minliang Li ; Song Yu ; Jie Yang ; Zhixiao Chen ; Yi Han ; Wanyi Gu

Author_Institution

State Key Lab. of Inf. Photonics & Opt. Commun., Beijing Univ. of Posts & Telecommun., Beijing, China

Volume

5

Issue

6

fYear

2013

fDate

Dec. 2013

Firstpage

7800312

Lastpage

7800312

Abstract

The M-ary support vector machine (SVM) is introduced as a nonparameter nonlinear phase noise (NLPN) mitigation approach for the coherent optical systems. The NLPN tolerance of the system can be improved by using the M-ary SVM to conduct nonlinear detection. In this scheme, SVMs with different classification strategies are utilized to execute binary classification for signals impaired by fiber NLPN. Since the separating hyperplane of each SVM is constructed by training data, this scheme is independent from the knowledge of the transmission link. In numerical simulation, the M-ary SVM performs better than the method of amplitude-dependent phase rotation at the transmitter and receiver, as well as the maximum likelihood detection with back rotation.

Keywords

light coherence; maximum likelihood detection; numerical analysis; phase noise; support vector machines; M-ary SVM; M-ary support vector machine; NLPN tolerance; amplitude-dependent phase rotation; binary classification; coherent optical systems; maximum likelihood detection; nonlinear detection; nonparameter nonlinear phase noise mitigation; numerical simulation; training data; transmission link; Nonlinear optics; Optical noise; Phase noise; Support vector machines; Training; Training data; Vectors; M-ary SVM; Nonlinear phase noise; quadratic amplitude modulation (QAM); support vector machine (SVM);

fLanguage

English

Journal_Title

Photonics Journal, IEEE

Publisher

ieee

ISSN

1943-0655

Type

jour

DOI

10.1109/JPHOT.2013.2287565

Filename

6648652