Optical filtering impairment monitoring based on model fusion for optical networks

Abstract

Reconfigurable optical add-and-drop multiplexer (ROADM) is a key element in optical networks. As several ROADMs are cascaded over long paths, the penalty induced by ROADM has become non-negligible due to the tight optical filtering. In this case, for efficient and reliable network planning and operation, accurate monitoring of optical filtering penalty is very important. In this paper, we propose a real-time optical filtering monitoring scheme based on model fusion. We combine an analytical model based on the digital communications theory of band-limited channels with linear equalization and a data-driven model implemented using artificial neural network (ANN). The scheme can achieve high accuracy and interpretability. Moreover, since the input features are extracted from configuration parameters and receiver digital signal processing (DSP), no additional devices are needed, which is attractive for practical deployment. Extensive simulations and experiments are conducted to investigate the performance of the scheme, and the results show the superior performance.