Optical filtering penalty estimation using artificial neural network in elastic optical networks with cascaded reconfigurable optical add-drop multiplexers

For future elastic optical networks, the narrow filtering effect induced by cascaded reconfigurable optical add-drop multiplexers (ROADMs) is one of the major impairments. It is essential to accurately estimate the filtering penalty to minimize network margins and optimize resource utilization. We present a method for estimating filtering penalty using machine learning (ML). First, we investigate the impact of ROADM location distribution and bandwidth allocation on the narrow filtering effect. Afterward, an ML-aided approach is proposed to estimate the filtering penalty under various link conditions. Extensive simulations with 9600 links are implemented to demonstrate the superior performance of the proposed scheme.