论文信息 - Nonlinear decision boundary created by a machine learning-based classifier to mitigate nonlinear phase noise

Nonlinear decision boundary created by a machine learning-based classifier to mitigate nonlinear phase noise

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.

[1] J. Kahn,et al. Signal Design and Detection in Presence of Nonlinear Phase Noise , 2007, Journal of Lightwave Technology.

[2] H. M. V. R. Herath,et al. Effect of Nonlinear Phase Noise on the Performance of $M$-Ary PSK Signals in Optical Fiber Links , 2013, Journal of Lightwave Technology.

[3] Maxim Kuschnerov,et al. Reduced Complexity Digital Back-Propagation Methods for Optical Communication Systems , 2014, Journal of Lightwave Technology.

[4] James A. Bucklew,et al. Support vector machines and the multiple hypothesis test problem , 2001, IEEE Trans. Signal Process..

[5] Darko Zibar,et al. Machine learning concepts in coherent optical communication systems , 2014 .