Optimized management of ultra-wideband photonics switching systems assisted by machine learning.

Recent years have seen an unprecedented growth of data traffic driven by a continuous increase of connected devices and new applications. This trend will tend to saturate transparent optical networks that are the backbone of the whole telecommunication infrastructure. To improve the capacity of already deployed network infrastructures and maximize operators CAPEX returns, band-division multiplexing (BDM) has emerged as a promising solution to expand the fiber bandwidth beyond the existing C-band. Along with this, the demand for flexible and dynamically reconfigurable functionalities in each network layer is increasing. In this regard, optical networking is fast evolving towards the applications of the software-defined networking (SDN) paradigm down to the physical layer. The implementation of optical SDN requires the full abstraction and virtualization of each network element in order to enable complete control by a centralized network controller. To pursue this objective, photonics transmission components and their transmission functionalities must be abstracted to allow the definition of the control states and a real-time quality-of-transmission (QoT) evaluation of transparent lightpaths (LP). In this work, we propose an SDN based model of a photonic switching fabric that allows determining the control state and evaluating QoT degradation. Our investigations present a wideband optical switch design based on photonic integrated circuits (PICs), where QoT degradation is abstracted using a structure-agnostic approach based on machine learning (ML). The ML engine training and testing datasets are generated synthetically by software simulation of the photonic switch architecture. Results show the potential of the proposed technique to predict QoT impairments with high accuracy, and we envision its application in a real-time control plane.