Assessment of Linearity Improvement in Optical Communication Systems with Machine Learning Methods

Muhammad Usman Hadi

doi:10.20944/preprints201910.0008.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 30 September 2019 / Approved: 2 October 2019 / Online: 2 October 2019 (03:09:31 CEST)

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.

radio over fiber; nonlinearities mitigation; support vector machine method; RL-SARSA

Physical Sciences, Optics and Photonics

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Assessment of Linearity Improvement in Optical Communication Systems with Machine Learning Methods

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