Akademik Veri Yönetim Sistemi
Optics and Laser Technology, cilt.197, 2026 (SCI-Expanded, Scopus)
In this research, four different machine learning (ML) algorithms have been used to predict the optical signal-to-noise ratio (OSNR) at central channels of dense and ultra-dense wavelength division multiplexing (DWDM/UDWDM) communication systems under the triple impact of four-wave mixing (FWM), stimulated Raman scattering (SRS), and amplified spontaneous emission (ASE) noise. The ML algorithms used in the research are Gaussian Process Regression (GPR), Narrow Neural Network (N-NN), Boosted Trees (BT), and Quadratic Support Vector Machines (QSVM). 7-, 15-, 31- and 63-channel configurations have been considered for DWDM/UDWDM systems. Two different datasets have been created to predict the OSNR value. The datasets used to predict the OSNR value have been created considering DWDM/UDWDM system configurations with 3.125 GHz-100 GHz channel spacing range and 1, 2, 4, and 5 erbium-doped fiber amplifiers (EDFAs). For the first data set, where the channel input powers have been varied between 0.1 mW and 5 mW, OSNR values have been predicted with the best goodness-of-fit metrics using GPR. For the second data set, where the total transmission length has been varied between 1 km and 150 km, OSNR values have been predicted with the best goodness-of-fit metrics using N-NN for 7-channel DWDM/UDWDM systems and GPR for 15-, 31-, and 63-channel DWDM/UDWDM systems. The detailed analysis presented in this paper will provide a new perspective on estimating the performance of AI integrated WDM-based transmission systems.