Precise computation of the quality of transmission (QoT) of lightpaths (LPs) in transparent optical networks has techno-economic importance for any network operator. The QoT metric of LPs is defined by the generalized signal-to-noise ratio (GSNR), which includes the effects of both amplified spontaneous emission noise and nonlinear interference accumulation. Generally, the physical layer of a network is characterized by nominal values provided by vendors for the operational parameters of each network element (NE). Typically, NEs suffer a variation in the working point that implies an uncertainty from the nominal value, which creates uncertainty in the GSNR computation and requires the deployment of a system margin. We propose the use of a machine learning agent trained on a dataset from an in-service network to reduce the uncertainty in the GSNR computation on an unused sister network, based on the same optical transport equipment and thus following the transfer learning paradigm. We synthetically generate datasets for both networks using the open-source library GNPy and show how the proposed deep neural network based on TensorFlow may substantially reduce the GSNR uncertainty and, consequently, the needed margin. We also present a statistical analysis of the observed GSNR fluctuations, showing that the per-wavelength GSNR distribution is always well-approximated as Gaussian, enabling a statistical closed-form approach to the margin setting.

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.

With the advancement in evolving concepts of software-defined networks and elastic-optical-network, the number of design parameters is growing dramatically, making the lightpath (LP) deployment more complex. Typically, worst-case assumptions are utilized to calculate the quality-of-transmission (QoT) with the provisioning of high-margin requirements. To this aim, precise and advanced estimation of the QoT of the LP is essential for reducing this provisioning margin. In this investigation, we present convolutional-neural-networks (CNN) based architecture to accurately calculate QoT before the actual deployment of LP in an unseen network. The proposed model is trained on the data acquired from already established LP of a completely different network. The metric considered to evaluate the QoT of LP is the generalized signal-to-noise ratio (GSNR). The synthetic dataset is generated by utilizing well appraised GNPy simulation tool. Promising results are achieved, showing that the proposed CNN model considerably minimizes the GSNR uncertainty and, consequently, the provisioning margin.

We present a thorough machine-learning framework based on real-time state-of-polarization (SOP) monitoring for robust anomaly identification in optical fiber networks. We exploit SOP data under three different threat scenarios: (i) malicious or critical vibration events, (ii) overlapping mechanical disturbances, and (iii) malicious fiber tapping (eavesdropping). We used various supervised machine learning techniques like k-Nearest Neighbor (k-NN), random forest, extreme gradient boosting (XGBoost), and decision trees to classify different vibration events. We also assessed the framework’s resilience to background interference by superimposing sinusoidal noise at different frequencies and examining its effects on the polarization signatures. This analysis provides insight into how subsurface installations, subject to ambient vibrations, affect detection fidelity. This highlights the sensitivity to which external interference affects polarization fingerprints. Crucially, it demonstrates the system’s capacity to discern and alert on malicious vibration events even in the presence of environmental noise. However, we focus on the necessity of noise-mitigation techniques in real-world implementations while providing a potent, real-time mechanism for multi-threat recognition in the fiber networks.

Nowadays, optical transport and data center networks utilize wavelength-division multiplexing (WDM) and band division multiplexing (BDM) as possible solutions to increase the capacity to support the ever-increasing traffic demand. It is an alternative solution to simple spatial division multiplexing (SDM) intended to utilize more than one fiber per link. In this perspective, we propose a new modular photonic integrated multi-band wavelength selective switch (WSS) to implement a novel reconfigurable optical add-drop multiplexer (ROADM) architecture. The proposed WSS can operate on a broad spectrum range, covering the three S+C+L bands, and is potentially scalable to large numbers of output fibers and routed channels while maintaining a small footprint. We investigated the network performance assessment of the ROADM architecture based on the proposed multi-band WSS on a German network, and a detailed comparison is given for both SDM and BDM solutions. Results show that the increase in network capacity by exploiting more bands in the cost-efficient BDM scenario is quite close to the SDM scenario using existing infrastructure replicated over multiple fibers.

Optical networking is fast evolving towards the applications of the Software-defined Networking (SDN) paradigm down to the (Wavelength-division Multiplexing) WDM transport layer for cost-effective and flexible infrastructure management. Optical SDN requires each network element's software abstraction to enable full control by the centralized network controller. Nowadays, modern network elements, especially photonic switching systems, are developed by exploiting the fast-emerging technology of Photonic Integrated Circuit (PIC) that consists of complex fabrics of elementary units that can be driven individually using a large set of elementary controls. In this work, we focus on modeling the elementary control states of the topological structures behind PIC N ×N switches under a fully blind approach based on Machine Learning (ML) techniques. The ML agent's training and testing datasets are obtained synthetically by software simulation of the photonic switch structure. The proposed technique's scalability and accuracy are validated by considering different dimensions N and applying it to two different switching topologies: the Honey-Comb Rearrangeable Optical Switch and the Beneš network. Excellent results in terms of prediction of the control states are achieved for both of the considered topologies.