Machine learning-based Raman amplifier design

A multi-layer neural network is employed to learn the mapping between Raman gain profile and pump powers and wavelengths. The learned model predicts with high-accuracy, low-latency and low-complexity the pumping setup for any gain profile.Comment: conferenc

Introduction to the JOCN Special Issue on Open Optical Networks

We provide an introduction to the paradigm of open optical networks (OON), including its evolution and benefits, followed by a brief summary of the papers in the OON special issue

Synergetical use of analytical models and machine-learning for data transport abstraction in open optical networks

The key-operation to enabling an effective data transport abstraction in open optical line systems (OLS) is the capability to predict the quality of transmission (QoT), that is given by the generalized signal-to-noise ratio (GSNR), including both the effects of the ASE noise and the nonlinear interference (NLI) accumulation. Among the two impairing effects, the estimation of the ASE noise is the most challenging task, because of the spectrally resolved working point of the erbium-doped fiber amplifiers (EDFA) depending on the spectral load, given the overall gain. While, the computation of the NLI is well addressed by mathematical models based on the knowledge of parameters and spectral load of fiber spans. So, the NLI prediction is mainly impaired by the uncertainties on insertion losses an spectral tilting. An accurate and spectrally resolved GSNR estimation enables to optimize the power control and to reliably and automatically deploy lightpaths with minimum margin, consequently maximizing the transmission capacity. We address the potentialities of machine-learning (ML) methods combined with analytic models for the NLI computation to improve the accuracy in the QoT estimation. We also analyze an experimental data-set showing the main uncertainties and addressing the use of ML to predict their effect on the QoT estimation

Band-Division vs. Space-Division Multiplexing: A Network Performance Statistical Assessment

We compare the networking merit of two possible multiplexing techniques on top of wavelength division multiplexing to enlarge transmission capacity: the band division multiplexing (BDM) that aims at using up to all the U-to-O low-loss transmission bands available on the G-652.D fiber and the spatial division multiplexing (SDM) implemented by activating additional fibers, used on the C-band only. We use the statistical network assessmemnt process (SNAP) to derive the networking performance as blocking probability vs. the total allocated traffic normalized with respect to the multiplexing cardinality. We analyze two network topologies: the German regional network and the US-NET continental network. In case dark fibers are available, SDM upgrades are always the best solution, enabling up to 12% and 17% of extra traffic at blocking probability equal to 10^{-3} on top of the multiplication by the multiplexing cardinality (N_{ ext{M}}) of 12, for the German and US-NET topology, respectively. BDM solutions present worse performance, but mixed BDM/SDM solutions display quite limited penalties with respect to the pure SDM solution, up to the use of 16 THz per fiber. So, mixed BDM/SDM implementation seems the most convenient solution in case of limited availability of dark fibers. Pure BDM solutions occupying a bandwidth larger than 16 THz display an increasingly and considerable gap in the allocated traffic with respect to the pure BDM, therefore, their use must be considered only in case of total absence of available dark fibers

QoT Computation for 100G Lightpaths Routed on 10G-loaded Dispersion-Managed Network Segments

The core and backbone optical network market segment is largely dominated by coherent transmission delivering 100Gbps and beyond thanks to the DSP-based coherent transceivers technology optical line systems without chromatic dispersion compensation. The metro and access segment instead is still often made of dispersion-compensated optical line systems operated with cheap 10G transceivers because of the still excessive CAPEX required to upgrade this segment to coherent technology. In the context of the gradual rise of SDN technology, aimed at dynamically, transparently and automatically managing and orchestrating optical networks, the ability to route 100G coherent channels through a section of dispersion managed network populated with legacy 10G channels enables more flexibility and CAPEX savings. In this work we propose a simple, fast and conservative quality-of-transmission estimator, tailored to the needs of a software module for optical path computation, able to estimate of the 10G-to-100G non-linear effects

Statistical assessment of open optical networks

In order to cope with the increase of the final user traffic, operators and vendors are pushing towards physical layer aware networking as a way to maximize the network capacity. To this aim, optical networks are becoming more and more open by exposing physical parameters enabling fast and reliable estimation of the lightpath quality of transmission. This comes in handy not only from the point of view of the planning and managing of the optical paths but also on a more general picture of the whole optical network performance. In this work, the Statistical Network Assessment Process (SNAP) is presented. SNAP is an algorithm allowing for estimating different network metrics such as blocking probability or link saturation, by generating traffic requests on a graph abstraction of the physical layer. Being aware of the physical layer parameters and transceiver technologies enables assessing their impact on high level network figures of merit. Together with a detailed description of the algorithm, we present a comprehensive review of several results on the networking impact of multirate transceivers, flex-grid spectral allocation as a means to finely exploit lightpath capacity and of different Space Division Multiplexing (SDM) solutions