Supervised Machine Learning Method based Modulation Formats Classification for WDM Systems

Recently, Machine Learning (ML) play a significant role in the design and optimization of new generation optical systems that are intelligent, adaptive, and efficient, enabling a wide range of new applications in the field of optical telecommunications. In this work, ML algorithm is proposed to classify the two common intensity modulation formats, such as Non-Return-to-Zero (NRZ) and Return-to-Zero (RZ) used in Wavelength Division Multiplexing (WDM) system. ML-based modulation classification involves to classify a signal based on its features that can include statistical features, such as the mean and variance of the signal, spectral features such as the power spectral density (PSD), autocorrelation function, constellation diagram, Bit Error Rate (BER), Q-Factor and Signal-to-Noise Ratio (SNR). The proposed system was trained on a dataset of signals with the known modulation schemes, NRZ and RZ which is pre-processed to extract features, notably BER, Q- Factor and SNR. Once the dataset is prepared, an ML algorithm was trained and will output a predicted modulation format based on the learned relationship between the features and the modulation format to classify new signals. The obtained results based on Pearson correlation matrix and VIF (Variance Inflation Factor) factor indicate that from a dataset of Q-Factor as a function of the distance link, the ML-based modulation classification help in optimizing the design of WDM systems by allowing for more precise control and tuning of the used modulation format.