Version 1
: Received: 30 September 2019 / Approved: 2 October 2019 / Online: 2 October 2019 (03:09:31 CEST)
How to cite:
Hadi, M.U. Assessment of Linearity Improvement in Optical Communication Systems with Machine Learning Methods. Preprints2019, 2019100008. https://doi.org/10.20944/preprints201910.0008.v1
Hadi, M.U. Assessment of Linearity Improvement in Optical Communication Systems with Machine Learning Methods. Preprints 2019, 2019100008. https://doi.org/10.20944/preprints201910.0008.v1
Hadi, M.U. Assessment of Linearity Improvement in Optical Communication Systems with Machine Learning Methods. Preprints2019, 2019100008. https://doi.org/10.20944/preprints201910.0008.v1
APA Style
Hadi, M.U. (2019). Assessment of Linearity Improvement in Optical Communication Systems with Machine Learning Methods. Preprints. https://doi.org/10.20944/preprints201910.0008.v1
Chicago/Turabian Style
Hadi, M.U. 2019 "Assessment of Linearity Improvement in Optical Communication Systems with Machine Learning Methods" Preprints. https://doi.org/10.20944/preprints201910.0008.v1
Abstract
Use of Machine Learning (ML) methodologies in optical communications has paved a new pathway. In this paper, firstly, we discuss the use of ML methodologies for reducing optical fiber nonlinearities, nonlinearity compensation, fault detection and optical performance monitoring. Then we present our recent work where we compare RL-SARSA and SVM based method with conventional method. The results show that RL-SARSA and SVM methods are successful candidates in mitigating the nonlinearities in proposed system as compared to conventional optical communication system.
Keywords
radio over fiber; nonlinearities mitigation; support vector machine method; RL-SARSA
Subject
Physical Sciences, Optics and Photonics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
