Mitigation of nonlinearities in analog radio over fiber links using machine learning approach

Muhammad Usman Hadi

doi:10.1016/j.icte.2020.11.002

Mitigation of nonlinearities in analog radio over fiber links using machine learning approach

Muhammad Usman Hadi

Faculty Of Computing, Eng. & Built Env.

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

16 Downloads (Pure)

Abstract

Machine learning (ML) techniques are looked upon as an innovative and realistic direction to cope up with nonlinearity issues in fiber optics communication. In this paper, a 64-quadrature amplitude modulation (QAM) based radio over fiber (RoF) system is demonstrated for 10 km of standard single mode fiber length utilizing support vector machine (SVM) method to indicate an effective nonlinearity mitigation in front-hauls. The comparison of SVM is drawn with conventional ML classifiers to optimize symbol decision boundary that will reduce the RoF link impairments. The results are reported in terms of BER, Eye-linearity and Quality factor.

Original language	English
Pages (from-to)	253-258
Number of pages	6
Journal	ICT Express
Volume	7
Issue number	2
Early online date	16 Nov 2020
DOIs	https://doi.org/10.1016/j.icte.2020.11.002
Publication status	Published - 1 Jun 2021

Bibliographical note

Publisher Copyright:
© 2021 The Korean Institute of Communications and Information Sciences (KICS)

Keywords

Eye-linearity
Machine learning
Radio over fiber
SVM

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10.1016/j.icte.2020.11.002Licence: CC BY-NC-ND

1-s2.0-S2405959520304768-mainFinal published version, 1.2 MBLicence: CC BY-NC-ND

Cite this

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AB - Machine learning (ML) techniques are looked upon as an innovative and realistic direction to cope up with nonlinearity issues in fiber optics communication. In this paper, a 64-quadrature amplitude modulation (QAM) based radio over fiber (RoF) system is demonstrated for 10 km of standard single mode fiber length utilizing support vector machine (SVM) method to indicate an effective nonlinearity mitigation in front-hauls. The comparison of SVM is drawn with conventional ML classifiers to optimize symbol decision boundary that will reduce the RoF link impairments. The results are reported in terms of BER, Eye-linearity and Quality factor.

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Mitigation of nonlinearities in analog radio over fiber links using machine learning approach

Abstract

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Keywords

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