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 | |
Publication status | Published (in print/issue) - 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