Machine learning-based regression models for predicting signal quality of dense wavelength division multiplexing (DWDM) optical communication network

Over the years, optical communication systems have been a significant source of fast and secure communication. However, factors like noise and mitigation error can degrade the bit error rate (BER) and quality factor (Q factor) of optical communication systems. Predicting the optimal threshold, Q factor, and BER is usually a difficult task. Therefore, in this paper, machine learning-based linear regression, least absolute shrinkage and selection operator (LASSO) regression, and Ridge regression have been used for a dense wavelength division multiplexing (DWDM)-based optical communication network to predict the signal quality. These techniques have been used to predict the desired BER, Q factor, threshold, and eye height of the system. To demonstrate this research concept, a DWDM-based optical communication network of 50 km length is designed and simulated using Optisystem-14.0. After data preparation, regression models have been developed and validated through diagnostic plots. Results show that mean square error (MSE) has a significant decline with an increase in the number of epochs for all four models. LASSO and Ridge regression have effectively resolved the issue of overfitting, which occurred in the linear regression case. Furthermore, the mean MSE plot proved the significant reduction of mean MSE in the case of LASSO regression. Results show that min BER for LASSO regression came out to be -173,627.14, providing a robust and cost-efficient process.