Analysis of the Performance for Quality of Transmission in Optical Fiber Communication Based on Machine Learning Optimization
Abstract
The foundation and application of optical communication networks is the estimation of the optical signal’s Quality of Transmission (QoT) parameters from source to destination nodes. However, Machine Learning (ML) approaches are being used in recent research to increase the accuracy of QoT estimation. In this study, the performance validation was carried out through the application of ML computational technique and the results demonstrated a stable Bit Error Ratio (BER) of ~10?¹? and a consistent Signal-to-Noise Ratio (SNR) of 12.6–13.2 dB across transmission distances 19–22 km. In addition, the Phase-Shift Keying (PSK) and the Quadrature Phase-Shift Keying (QPSK) modulation outperformed the Amplitude Shift Keying (ASK) and Frequency Shift Keying (FSK), with statistical significance confirmed via chi-square tests. Conclusively, comparative advantages surpassed prior studies in BER reduction, SNR efficiency and multi-modulation adaptability.
Keywords
Artificial Intelligence; Communication; Optical fiber; Machine learning; Modelling; Transmission
Citations
IRE Journals:
Folakemi Judith Omogoroye , Guiawa Mathurine , Olubunmi Adewale Akinola , Onyegbadue Ikenna Augustine , Fred Izilein
"Analysis of the Performance for Quality of Transmission in Optical Fiber Communication Based on Machine Learning Optimization" Iconic Research And Engineering Journals Volume 9 Issue 2 2025 Page 873-879
IEEE:
Folakemi Judith Omogoroye , Guiawa Mathurine , Olubunmi Adewale Akinola , Onyegbadue Ikenna Augustine , Fred Izilein
"Analysis of the Performance for Quality of Transmission in Optical Fiber Communication Based on Machine Learning Optimization" Iconic Research And Engineering Journals, 9(2)
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 8:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12