Nonlinear decision boundary created by a machine learning-based classifier to mitigate nonlinear phase noise
Authors:
- 1 September 2015
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-3
Abstract
A machine learning-based classifier, namely SVM, is introduced to create the nonlinear decision boundary in M-ary PSK-based coherent optical system to mitigate NLPN. The maximum transmission distance and LPRD tolerance are improved by 480 km and 3.3 dBm for 8PSK.Keywords
This publication has 4 references indexed in Scilit:
- Reduced Complexity Digital Back-Propagation Methods for Optical Communication SystemsJournal of Lightwave Technology, 2014
- Effect of Nonlinear Phase Noise on the Performance of $M$-Ary PSK Signals in Optical Fiber LinksJournal of Lightwave Technology, 2012
- Signal Design and Detection in Presence of Nonlinear Phase NoiseJournal of Lightwave Technology, 2007
- Support vector machines and the multiple hypothesis test problemIEEE Transactions on Signal Processing, 2001