OPTICAL SAFETY AND CONNECTIONS VERIFICATION

Techniques are described herein to verify and intercept any intra-node mis-cabling between cards. These techniques do not require any additional hardware, distributed protocol, or intelligence in the network manager or Software Defined Networking (SDN) tools

Flexible and Autonomous Multi-band Raman Amplifiers

We propose an embedded controller able to autonomously manage Raman amplification in software-defined optical networks. The conceived structure allows the system to work both in single and multi-band transmission, achieving a large range of amplification constraints. A set of experiments validates this proposal

Autonomous Raman Amplifiers in Software-Defined Optical Transport Networks

Within a context of software-defined optical transport networks (SD-OTN), this work addresses specifically the management of Raman amplification, aiming to introduce and experimentally validate a system able to autonomously control this feature in-situ. In particular, given the required amplification constraints, an ad-hoc software module has been developed in order to optimize Raman pump power levels. Then, relying on this software, the architecture of an embedded controller to install on board the Raman card has been defined to handle Raman pumps. The use of a conceived probing procedure allows to self-adapt each Raman amplifier to the installed fiber, allowing it to autonomously operate at the working point required by the control plane. Relying on the system telemetry, the proposed architecture controls the Raman pumps in order to achieve the required amplification constraints in terms of average gain and tilt. The entire proposal is validated through a set of experimental measurements that proofs both the achievement of the required gain target and the importance of the probing phase procedure in making the Raman amplifier autonomous and self-adaptable

Autonomous Physical Layer Characterization in Cognitive Optical Line Systems

We develop a procedure to autonomously characterize the optical line system physical layer, span-by-span, using in-line OTDRs and OCMs. This procedure has been experimentally validated, showing a clear correlation between the experimental outcomes and emulations

Local vs. Global Optimization for Optical Line System Control in Disaggregated Networks

Setting the operating point of optical amplifiers of optical line systems (OLS)s within transparent, disaggregated and reconfigurable networks is a crucial task that determines the optical transmission performance of the specific infrastructure. In this work, four optimization strategies for OLS control are compared through a simulation campaign, where a realistic physical layer is replicated using a machine-learning model derived from an experimental dataset on commercial devices for the Erbium-doped fiber amplifiers (EDFA)s and a characterized set of fiber spans. In particular, two distinct objective functions are evaluated, both at the end of the line (global approach), and, in turn, at the end of each single span (local approach)

Gain profile characterization and modelling for an accurate EDFA abstraction and control

Relying on a two-measurement characterization phase, a gain profile model for dual-stage EDFAs is presented and validated in full spectral load condition. It precisely reproduces the EDFA dynamics varying the target gain and tilts parameters as shown experimentally on two commercial items from different vendors

Enhancing Lightpath QoT Computation with Machine Learning in Partially Disaggregated Optical Networks

Increasing traffic demands are causing network operators to adopt disaggregated and open networking solutions to better exploit optical transmission capacity, and consequently enable a software-defined networking (SDN) approach to control and management that encompasses the WDM data transport layer. In these frameworks, a quality of transmission estimator (QoT-E) that gives the generalized signal-to-noise ratio (GSNR) is commonly used to compute the feasibility of transparent lightpaths (LP)s, taking into account the amplified spontaneous emission (ASE) noise and the nonlinear interference (NLI). In general, the ASE noise is the main contributor to the GSNR and is also the most challenging noise component to evaluate in a scenario with varying spectral loads, due to fluctuations in the optical amplifier responses. In this work, we propose a machine learning (ML) algorithm that is trained using different ASE-shaped spectral loads in order to predict the OSNR component of the GSNR; this methodology is subsequently used in combination with a QoT-E in the lightpath computation engine (L-PCE). We present an experiment on a point-to-point optical line system (OLS), including 9 commercial erbium-doped fiber amplifiers (EDFA)s used as black-boxes, each with variable gain and tilt values, and 8 fibers that are characterized by distinct physical parameters. Within this experiment, we receive the signal at the end of the OLS, measuring the bit-error-rate (BER) and the power spectrum, over 2520 different spectral loads. From this dataset, we extract the expected GSNRs and their linear and nonlinear components. Through joint application of a ML algorithm and the open-source GNPy library, we obtain a complete QoT-E, demonstrating that a reliable and accurate LP feasibility predictor may be implemented

ML-Based Spectral Power Profiles Prediction in Presence of ISRS for Ultra-Wideband Transmission

A generalized method based on machine learning (ML) and artificial neural networks (ANNs) is proposed for a fast and accurate prediction of spectral and spatial evolution of power profiles in support of performance and quality-of-transmission (QoT) real-time assessment of ultra-wideband links. These systems, operating on bandwidths larger than the standard C–band, are affected by inter-channel stimulated Raman scattering (ISRS), whose impact on power profiles evolution along the fiber is generally estimated by solving numerically a set of nonlinear ordinary differential equations (ODEs). However, the computational effort, in terms of complexity and convergence time to the solution, increases with the bandwidth and the number of transmitted wavelength division multiplexing (WDM) channels, which makes the usual approach no longer particularly suitable to operate in real time. To meet the speed requirements, three different ANNs are introduced to make fast predictions of power profiles over frequency and distance considering a wide range of scenarios: different power per channel values, different fiber types and different span lengths. Two ANNs are used on synthetic data to estimate the impact of linear and nonlinear fiber impairments in support of system modeling. Specifically, one to directly predict the evolution of spectral power profiles along the fiber and the other to estimate the coefficients to insert in a closed-form version of the EGN model. A third ANN operates on experimental data and it is used to predict power profiles at the end of the fiber for fast estimations of system performance. The obtained results show highly accurate predictions with values of maximum absolute error, computed between predicted and actual power profiles, not exceeding 0.2 dB for ∼97% of cases for synthetic data and always below 0.5 dB for experimental data. Such results prove the potential of the proposed approach making it suitable for real time application of QoT estimation