Conference paper Open Access
Locatelli, Fabiano; Christodoulopoulos, Konstantinos; Fàbrega, Josep M.; Svaluto- Moreolo, Michela; Nadal, Laia; Spadaro, Salvatore
Channel spectral monitors are becoming a cost effective solution to improve the management, resiliency and efficiency of next generation optical transport networks. We experimentally demonstrate a technique based on machine learning (ML) for the in-band estimation of amplified spontaneous emission (ASE) noise and filter 3-dB bandwidth, using optical spectra acquired after the reconfigurable optical add/drop multiplexers (ROADMs) filters. We assess the performance of the proposed method, considering laser drift and filters bandwidth tightening scenarios, showing quite good estimation accuracy under such conditions.