Machine learning (ML) methodologies gave an innovative and realistic direction to cope up with nonlinearity issues in fiber optics communication. In this paper, a 40-Gb/s 128-quadrature amplitude modulation (QAM) signal based Radio over Fiber (RoF) system is experimentally evaluated for 70 km of standard single mode fiber length which utilizes support vector machine (SVM) decision method to indicate an effective nonlinearity mitigation. The influence of different impairments in the system is evaluated that includes the influences of Mach-Zehnder Modulator nonlinearities, in-phase and quadrature phase skew of the modulator, input signal power and noise due to amplified spontaneous emission. By employing SVM, the results demonstrated in terms of bit error rate and eye linearity suggest that impairments are significantly reduced and licit input signal power span of 5dBs is enlarged to 15 dBs.
|Title of host publication||Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published (in print/issue) - 5 Nov 2020|
|Event||23rd IEEE International Multi-Topic Conference, INMIC 2020 - Bahawalpur, Pakistan|
Duration: 5 Nov 2020 → 7 Nov 2020
|Name||Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020|
|Conference||23rd IEEE International Multi-Topic Conference, INMIC 2020|
|Period||5/11/20 → 7/11/20|
Bibliographical notePublisher Copyright:
© 2020 IEEE.
- Nonlinearity Mitigation
- Radio over Fiber
- Support Vector Machine