Multi-layer optical networks are a prospective concept in the upcoming era of bandwidth-abundant optical networks that decouple the enormous spatial division multiplexing (SDM) layer from the wavelength division multiplexing layer. In this direction, we present an analysis of a generalized multi-band optical cross-connect (OXC) at the networking level. This OXC operates in two stages: firstly, it groups wavelengths to form wavebands, and secondly, it switches them as waveband paths. The findings demonstrate that waveband routing is a viable and cost-effective replacement for traditional wavelength routing in massive parallel optical networks.
To reduce costs and simplify operations, network operators are deploying the latest network devices that are power efficient and compact. In this paper, a detailed comparison is made between the design of a reconfigurable add-drop multiplexer (ROADM) based on an integrated circuit (PIC) and the state-of-the-art ROADM devices. In particular, the performance of the device in terms of frequency response parameters is presented in the paper in comparison with the state-of-the-art ROADMs. The proposed PIC based ROADM can operate in a multi-band scenario, including C+L+S bands, and is potentially scalable to many output fibers and routed channels while maintaining a small footprint. A detailed network performance analysis is performed with the proposed PIC-based ROADM device and its impact on the network. Due to increasing traffic demand, the current optical transport infrastructure is experiencing capacity problems: two possible solutions are Spatial Division Multiplexing (SDM) and Bandwidth Division Multiplexing (BDM), which both allow capacity expansion of the existing infrastructure. We have studied the network performance of the proposed ROADM device on the Spain-E network and performed a detailed comparison for the SDM and BDM scenarios. Compared to the SDM approach, which requires the deployment of additional fiber, the cost-effective BDM scenario can better utilize capacity without installing new fiber infrastructure or using dark fibers.
We propose a novel modular photonic integrated Wavelength Selective Switch (WSS) based on a reconfigurable optical multiplexer architecture, capable to operate over the S+C+L bands and scalable. The densely integrated solution takes advantage of an input stage with grating assisted contra-directional couplers to separate channels in the three considered communication bands, followed by a cascade of two-stage ladder ring resonators, to separating each transmitted channel. A final switching stage routes the signal to the desired output fiber, with a cascade of thermally controlled Mach-Zehnder interferometers. The transmission penalty of the proposed solution has been evaluated in a coherent transmission scenario.
We propose a Machine Learning (ML) assisted procedure to extract Vertical Cavity Surface Emitting Lasers (VCSELs) parameters from Light-Current (L-I) and S21 curves using a two-step algorithm to ensure high accuracy of the prediction. In the first step, temperature effects are not included and a Deep Neural Network (DNN) is trained on a dataset of 10000 mean-field VCSEL simulations, obtained changing nine temperature-independent parameters. The agent is used to retrieve those parameters from experimental results at a fixed temperature. Secondly, additional nine temperature-dependent parameters are analyzed while keeping as constant the extracted ones and changing the operation temperature. In this way a second dataset of 10000 simulations is created and a new agent in trained to extract those parameters from temperature-dependent L-I and S21 curves.
Beneš networks represent an excellent solution for the routing of optical telecom signals in integrated, fully reconfigurable networks because of their limited number of elementary 2x2 crossbar switches and their nonblocking properties. Various solutions have been proposed to determine a proper Control State (CS) providing the required permutation of the input channels; since for a particular permutation, the choice is not unique, the number of cross-points has often been used to estimate the cost of the routing operation. This work presents an advanced version of this approach: we deterministically estimate all (or a reasonably large number of) the CSs corresponding to the permutation requested by the user. After this, the retrieved CSs are exploited by a data-driven framework to predict the Optical Signal to Noise Ratio (OSNR) penalty for each CS at each output port, finally selecting the CS providing minimum OSNR penalty. Moreover, three different data-driven techniques are proposed, and their prediction performance is analyzed and compared.
The proposed approach is demonstrated using 8x8 Beneš architecture with 20 ring resonator-based crossbar switches. The dataset of 1000 OSNRs realizations is generated synthetically for random combinations of the CSs using Synopsys® Optsim™ simulator. The computational cost of the proposed scheme enables its real-time operation in the field.
The ever-increasing demand for global internet traffic, together with evolving concepts of software-defined networks and elastic-optical-networks, demand not only the total capacity utilization of underlying infrastructure but also a dynamic, flexible, and transparent optical network. In general, worst-case assumptions are utilized to calculate the quality of transmission (QoT) with provisioning of high-margin requirements. Thus, precise estimation of the QoT for the lightpath (LP) establishment is crucial for reducing the provisioning margins. We propose and compare several data-driven machine learning (ML) models to make an accurate calculation of the QoT before the actual establishment of the LP in an unseen network. The proposed models are trained on the data acquired from an already established LP of a completely different network. The metric considered to evaluate the QoT of the LP is the generalized signal-to-noise ratio (GSNR), which accumulates the impact of both nonlinear interference and amplified spontaneous emission noise. The dataset is generated synthetically using a well-tested GNPy simulation tool. Promising results are achieved, showing that the proposed neural network considerably minimizes the GSNR uncertainty and, consequently, the provisioning margin. Furthermore, we also analyze the impact of cross-features and relevant features training on the proposed ML models’ performance.
In order to cope with the fast increase in data traffic demand, optical networks are fast evolving towards the disaggregation and progressive implementation of the openness paradigm. Such an evolution is enabling the application of the software-defined networking below the IP layer, down to the optical transmission (SD-OTN). SD-OTN is enabled by the capability of the network controller to automatized management of photonic switching systems, and allowing their full virtualization and softwarisation. To this purpose, one of the major matter of contention is an efficient utilization of routing strategies, which can be seamlessly incorporated into the control plane. In this work, we rely on data-driven science (DDS) to develop the machine learning (ML) model which is able to predict the routing strategies of generic N x N photonic switching system without any knowledge required of the topology. The dataset used for training and testing the ML model is generated “synthetically”. In particular, the training and testing of the proposed ML module is done in a completely topological and technological agnostic way and is able to perform its application in real-time. Furthermore, the scalability and accuracy of the proposed approach is verified by considering two different switching topologies: the Honey-Comb Rearrangeable Optical Switch and the Beneš network. Promising results are achieved in terms of predicting the control signals matrix for both of the considered topologies.
The increasing trend of global IP traffic is mainly driven by high-definition video services and cloud computing and storage. Moreover, to maintain a high quality of service in content delivery networking, data are geographically replicated in data centers distributed within network topologies. Thus, data centers are an emerging scenario for research and development aimed at energy-efficient transmission and networking solutions. Previous research work has focused on intradata center energy efficiency while interdata center energy issues have not been extensively analyzed yet. We propose heuristics and meta-heuristics for optimal placement of data centers with minimum power consumption over a network topology relying on flex-grid spectral use. In order to minimize the network’s power consumption, we have performed a detailed comparison of heuristic and meta-heuristic designs for different network scenarios based on real topologies. Moreover, our results show that meta-heuristic provides an optimum data center placement in a reasonable amount of time for a small- to medium-sized network.
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