Modeling filtering penalties in ROADM-based networks with machine learning for QoT estimation

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Monitoring 3dB bandwidth and other spectrum related parameters at ROADMs provides information about quality of their filters. We propose a machine-learning model to estimate end-to-end filtering penalty for more accurate QoT estimation of future connections.Authors would like to thank Karsten Schuh and Camille Delezoide of Nokia Bell Labs for technical discussionsonfilter modelling. This work is a part ofH2020-MSCA, ONFIRE project supported by EU, grant agreement No. 765275.Peer ReviewedPostprint (author's final draft

Exploiting optical signal analysis for autonomous communications

(English) Optical communications have been extensively investigated and enhanced in the last decades. Nowadays, they are responsible to transport all the data traffic generated around the world, from access to the core network segments. As the data traffic is increasing and changing in both type and patterns, the optical communications networks and systems need to readapt and continuous advances to face the future data traffic demands in an efficient and cost-effective way. This PhD thesis focuses on investigate and analyze the optical signals in order to extract useful knowledge from them to support the autonomous lightpath operation, as well as to lightpath characterization. The first objective of this PhD thesis is to investigate the optical transmission feasibility of optical signals based on high-order modulation formats (MF) and high symbol rates (SR) in hybrid filterless, filtered and flexible optical networks. It is expected a higher physical layer impairments impact on these kinds of optical signals that can lead to degradation of the quality of transmission. In particular, the impact of the optical filter narrowing arising from the node cascade is evaluated. The obtained simulation results for the required optical-signal-to-noise ratio in a cascade up to 10 optical nodes foresee the applicability of these kinds of optical signals in such scenarios. By using high-order MF and high SR, the number of the optical transponders cab be reduced, as well as the spectral efficiency is enhanced. The second objective focuses on MF and SR identification at the optical receiver side to support the autonomous lightpath operation. Nowadays, optical transmitters can generate several optical signal configurations in terms of MF and SR. To increase the autonomous operation of the optical receiver, it is desired it can autonomously recognize the MF and SR of the incoming optical signals. In this PhD thesis, we propose an accurate and low complex MF and SR identification algorithm based on optical signal analysis and minimum Euclidean distance to the expected points when the received signals are decoded with several available MF and SR. The extensive simulation results show remarkable accuracy under several realistic lightpath scenarios, based on different fiber types, including linear and nonlinear noise interference, as well as in single and multicarrier optical systems. The final objective of this PhD thesis is the deployment of a machine learning-based digital twin for optical constellations analysis and modeling. An optical signal along its lightpath in the optical network is impaired by several effects. These effects can be linear, e.g., the noise coming from the optical amplification, or nonlinear ones, e.g., the Kerr effects from the fiber propagation. The optical constellations are a good source of information regarding these effects, both linear and nonlinear. Thus, by an accurate and deep analysis of the received optical signals, visualized in optical constellations, we can extract useful information from them to better understand the several impacts along the crossed lightpath. Furthermore, by learning the different impacts from different optical network elements on the optical signal, we can accurately model it in order to create a partial digital twin of the optical physical layer. The proposed digital twin shows accurate results in modeled lightpaths including both linear and nonlinear interference noise, in several lightpaths configuration, i.e., based on different kind of optical links, optical powers and optical fiber parameters. In addition, the proposed digital twin can be useful to predict quality of transmission metrics, such as bit error rate, in typical lightpath scenarios, as well as to detect possible misconfigurations in optical network elements by cooperation with the software-defined networking controller and monitoring and data analytics agents.(Español) Las comunicaciones ópticas han sido ampliamente investigadas y mejoradas en las últimas décadas. En la actualidad, son las encargadas de transportar la mayoría del tráfico de datos que se genera en todo el mundo, desde el acceso hasta los segmentos de la red troncal. A medida que el tráfico de datos aumenta y cambia tanto en tipo como en patrones, las redes y los sistemas de comunicaciones ópticas necesitan readaptarse y avanzar continuamente para, de una manera eficiente y rentable, hacer frente a las futuras demandas de tráfico de datos. Esta tesis doctoral se centra en investigar y analizar las señales ópticas con el fin de extraer de ellas conocimiento útil para apoyar el funcionamiento autónomo de las conexiones ópticas, así como para su caracterización. El primer objetivo de esta tesis doctoral es investigar la viabilidad de transmisión de señales ópticas basadas en formatos de modulación de alto orden y altas tasas de símbolos en redes ópticas híbridas con y sin filtros. Se espera un mayor impacto de las degradaciones de la capa física en este tipo de señales ópticas que pueden conducir a la degradación de la calidad de transmisión. En particular, se evalúa el impacto de la reducción del ancho de banda del filtro óptico que surge tras atravesar una cascada de nodos. Los resultados de simulación obtenidos para la relación señal óptica/ruido requerida en una cascada de hasta 10 nodos ópticos prevén la aplicabilidad de este tipo de señales ópticas en tales escenarios. Mediante el uso de modulación de alto orden y altas tasas de símbolos, se reduce el número de transpondedores ópticos y se mejora la eficiencia espectral. El segundo objetivo se centra en la identificación de formatos de modulación y tasas de símbolos en el lado del receptor óptico para respaldar la operación autónoma de la conexión óptica. Para aumentar el funcionamiento autónomo del receptor óptico, se desea que pueda reconocer de forma autónoma la configuración de las señales ópticas entrantes. En esta tesis doctoral, proponemos un algoritmo de identificación de formatos de modulación y tasas de símbolos preciso y de baja complejidad basado en el análisis de señales ópticas cuando las señales recibidas se decodifican con varios formatos de modulación y tasas de símbolos disponibles. Los extensos resultados de la simulación muestran una precisión notable en varios escenarios realistas, basados en diferentes tipos de fibra, incluida la interferencia de ruido lineal y no lineal, así como en sistemas ópticos de portadora única y múltiple. El objetivo final de esta tesis doctoral es el despliegue de un gemelo digital basado en aprendizaje automático para el análisis y modelado de constelaciones ópticas. Una señal óptica a lo largo de su trayectoria en la red óptica se ve afectada por varios efectos, pueden ser lineales o no lineales. Las constelaciones ópticas son una buena fuente de información sobre estos efectos, tanto lineales como no lineales. Por lo tanto, mediante un análisis preciso y profundo de las señales ópticas recibidas, visualizadas en constelaciones ópticas, podemos extraer información útil de ellas para comprender mejor los diversos impactos a lo largo del camino propagado. Además, al aprender los diferentes impactos de los diferentes elementos de la red óptica en la señal óptica, podemos modelarla con precisión para crear un gemelo digital parcial de la camada física óptica. El gemelo digital propuesto muestra resultados precisos en conexiones que incluyen ruido de interferencia tanto lineal como no lineal, en varias configuraciones basados en diferentes tipos de enlaces ópticos, potencias ópticas y parámetros de fibra óptica. Además, el gemelo digital propuesto puede ser útil para predecir la calidad de las métricas de transmisión así como para detectar posibles errores de configuración en los elementos de la red óptica mediante la cooperación con el controlador de red, el monitoreo y agentes de análisis de datosPostprint (published version

GNPy model of the physical layer for open and disaggregated optical networking [Invited]

Networking technologies are fast evolving to support the request for ubiquitous Internet access that is becoming a fundamental need for the modern and inclusive society, with a dramatic speed-up caused by the COVID-19 emergency. Such evolution needs the development of networks into disaggregated and programmable systems according to the software-defined networking (SDN) paradigm. Wavelength-division multiplexed (WDM) optical transmission and networking is expanding as physical layer technology from core and metro networks to 5G x-hauling and inter- and intra-data-center connections requiring the application of the SDN paradigm at the optical layer based on the WDM optical data transport virtualization. We present the fundamental principles of the open-source project Gaussian Noise in Python (GNPy) for the optical transport virtualization in modeling the WDM optical transmission for open and disaggregated networking. GNPy approximates transparent lightpaths as additive white and Gaussian noise channels and can be used as a vendor-agnostic digital twin for open network planning and management. The quality-of-transmission degradation of each network element is independently modeled to allow disaggregated network management. We describe the GNPy models for fiber propagation, optical amplifiers, and reconfigurable add/drop multiplexers together with modeling of coherent transceivers from the back-to-back characterization. We address the use of GNPy as a vendor-agnostic design and planning tool and as physical layer virtualization in software-defined optical networking. (C) 2022 Optica Publishing Grou

Spectral processing techniques for efficient monitoring in optical networks

Having ubiquitous optical monitors in dense wavelength-division multiplexing (DWDM) or flex-grid networks allows the estimation in real time of crucial parameters. Such monitoring would be even more important in disaggregated optical networks, to inspect performance issues related to inter-vendor interoperability. Several important parameters can be retrieved using optical spectrum analyzers (OSAs). However, omnipresent OSAs represent an infeasible solution. Nevertheless, the advent of new, relatively cheap, compact and medium-resolution optical channel monitors (OCMs) enable a more intensive deployment of these devices. In this paper, we identify two main scenarios for the placement of such monitors: at the ingress and at the egress of the optical nodes. In the ingress scenario, we can directly estimate the parameters related to the signals, but not those related to the filters. On the contrary, in the egress scenario, the filter-related parameters can be easily detected, but not those related to amplified spontaneous emission. Therefore, we present two methods that, leveraging a curve fitting and a machine learning regression algorithm, allow detection of the missing parameters. We verify the proposed solutions with spectral data acquired in simulation and experimental setups. We obtained good estimation accuracy for both setups and for both studied placement scenarios. It is noteworthy that in the experimental assessment of the ingress scenario, we achieved a maximum absolute error (MAE) lower than 1 GHz in filter bandwidth estimation and a MAE lower than 0.5 GHz in filter frequency shift estimation. In addition, by comparing the relative errors of the considered parameters, we identified the ingress scenario as the more beneficial. In particular, we estimated the filter central frequency shift with 84% and the filter 6 dB bandwidth with 75% higher accuracy, with respect to datasheet/reference values. This translates into a total reduction of the estimated signal-to-noise ratio (SNR) penalty, introduced by a single optical filter, of 0.24 dB.Funding: Horizon 2020 Framework Programme (765275). This work is part of the Future Optical Networks for Innovation, Research and Experimentation (ONFIRE) project (https://h2020-onfire.eu), which is supported by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Action.Peer ReviewedPostprint (author's final draft

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

Study and application of spectral monitoring techniques for optical network optimization

One of the possible ways to address the constantly increasing amount of heterogeneous and variable internet traffic is the evolution of the current optical networks towards a more flexible, open, and disaggregated paradigm. In such scenarios, the role played by Optical Performance Monitoring (OPM) is fundamental. In fact, OPM allows to balance performance and specification mismatches resulting from the disaggregation adoption and provides the control plane with the necessary feedback to grant the optical networks an adequate automation level. Therefore, new flexible and cost-effective OPM solutions are needed, as well as novel techniques to extract the desired information from the monitored data and process and apply them. In this dissertation, we focus on three aspects related to OPM. We first study a monitoring data plane scheme to acquire the high resolution signal optical spectra in a nonintrusive way. In particular, we propose a coherent detection based Optical Spectrum Analyzer (OSA) enhanced with specific Digital Signal Processing (DSP) to detect spectral slices of the considered optical signals. Then, we identify two main placement strategies for such monitoring solutions, enhancing them using two spectral processing techniques to estimate signal- and optical filter-related parameters. Specifically, we propose a way to estimate the Amplified Spontaneous Emission (ASE) noise or its related Optical Signal-to-Noise (OSNR) using optical spectra acquired at the egress ports of the network nodes and the filter central frequency and 3/6 dB bandwidth, using spectra captured at the ingress ports of the network nodes. To do so, we leverage Machine Learning (ML) algorithms and the function fitting principle, according to the considered scenario. We validate both the monitoring strategies and their related processing techniques through simulations and experiments. The obtained results confirm the validity of the two proposed estimation approaches. In particular, we are able to estimate in-band the OSNR/ASE noise within an egress monitor placement scenario, with a Maximum Absolute Error (MAE) lower than 0.4 dB. Moreover, we are able to estimate the filter central frequency and 3/6 dB bandwidth, within an ingress optical monitor placement scenario, with a MAE lower than 0.5 GHz and 0.98 GHz, respectively. Based on such evaluations, we also compare the two placement scenarios and provide guidelines on their implementation. According to the analysis of specific figures of merit, such as the estimation of the Signal-to-Noise Ratio (SNR) penalty introduced by an optical filter, we identify the ingress monitoring strategy as the most promising. In fact, when compared to scenarios where no monitoring strategy is adopted, the ingress one reduced the SNR penalty estimation by 92%. Finally, we identify a potential application for the monitored information. Specifically, we propose a solution for the optimization of the subchannel spectral spacing in a superchannel. Leveraging convex optimization methods, we implement a closed control loop process for the dynamical reconfiguration of the subchannel central frequencies to optimize specific Quality of Transmission (QoT)-related metrics. Such a solution is based on the information monitored at the superchannel receiver side. In particular, to make all the subchannels feasible, we consider the maximization of the total superchannel capacity and the maximization of the minimum superchannel subchannel SNR value. We validate the proposed approach using simulations, assuming scenarios with different subchannel numbers, signal characteristics, and starting frequency values. The obtained results confirm the effectiveness of our solution. Specifically, compared with the equally spaced subchannel scenario, we are able to improve the total and the minimum subchannel SNR values of a four subchannel superchannel, of 1.45 dB and 1.19 dB, respectively.Una de las posibles formas de hacer frente a la creciente cantidad de tráfico heterogéneo y variable de Internet es la evolución de las actuales redes ópticas hacia un paradigma más flexible, abierto y desagregado. En estos escenarios, el papel que desempeña el modulo óptico de monitorización de prestaciones (OPM) es fundamental. De hecho, el OPM permite equilibrar los desajustes de rendimiento y especificación, los cuales surgen con la adopción de la desagregación; del mismo modo el OPM también proporciona al plano de control la realimentación necesaria para otorgar un nivel de automatización adecuado a las redes ópticas. En esta tesis, nos centramos en tres aspectos relacionados con el OPM. En primer lugar, estudiamos un esquema de monitorización para adquirir, de forma no intrusiva, los espectros ópticos de señales de alta resolución. En concreto, proponemos un analizador de espectro óptico (OSA) basado en detección coherente y mejorado con un específico procesado digital de señal (DSP) para detectar cortes espectrales de las señales ópticas consideradas. A continuación, presentamos dos técnicas de colocación para dichas soluciones de monitorización, mejorándolas mediante dos técnicas de procesamiento espectral para estimar los parámetros relacionados con la señal y el filtro óptico. Específicamente, proponemos un método para estimar el ruido de emisión espontánea amplificada (ASE), o la relación de señal-ruido óptica (OSNR), utilizando espectros ópticos adquiridos en los puertos de salida de los nodos de la red. Del mismo modo, estimamos la frecuencia central del filtro y el ancho de banda de 3/6 dB, utilizando espectros capturados en los puertos de entrada de los nodos de la red. Para ello, aprovechamos los algoritmos de Machine Learning (ML) y el principio de function fitting, según el escenario considerado. Validamos tanto las estrategias de monitorización como las técnicas de procesamiento mediante simulaciones y experimentos. Se puede estimar en banda el ruido ASE/OSNR en un escenario de colocación de monitores de salida, con un Maximum Absolute Error (MAE) inferior a 0.4 dB. Además, se puede estimar la frecuencia central del filtro y el ancho de banda de 3/6 dB, dentro de un escenario de colocación de monitores ópticos de entrada, con un MAE inferior a 0.5 GHz y 0.98 GHz, respectivamente. A partir de estas evaluaciones, también comparamos los dos escenarios de colocación y proporcionamos directrices sobre su aplicación. Según el análisis de específicas figuras de mérito, como la estimación de la penalización de la relación señal-ruido (SNR) introducida por un filtro óptico, demostramos que la estrategia de monitorización de entrada es la más prometedora. De hecho, utilizar un sistema de monitorización de entrada redujo la estimación de la penalización del SNR en un 92%. Por último, identificamos una posible aplicación para la información monitorizada. En concreto, proponemos una solución para la optimización del espaciado espectral de los subcanales en un supercanal. Aprovechando los métodos de optimización convexa, implementamos un proceso cíclico de control cerrado para la reconfiguración dinámica de las frecuencias centrales de los subcanales con el fin de optimizar métricas específicas relacionadas con la calidad de la transmisión (QoT). Esta solución se basa en la información monitorizada en el lado del receptor del supercanal. Validamos el enfoque propuesto mediante simulaciones, asumiendo escenarios con un diferente número de subcanales, distintas características de la señal, y diversos valores de la frecuencia inicial. Los resultados obtenidos confirman la eficacia de nuestra solución. Más específicatamente, en comparación con el escenario de subcanales igualmente espaciados, se pueden mejorar los valores totales y minimos de SNR de los subcanales de un supercanal de cuatro subcanales, de 1.45 dB y 1.19 dB, respectivamentePostprint (published version

Physical Layer Aware Optical Networks

This thesis describes novel contributions in the field of physical layer aware optical networks. IP traffic increase and revenue compression in the Telecom industry is putting a lot of pressure on the optical community to develop novel solutions that must both increase total capacity while being cost effective. This requirement is pushing operators towards network disaggregation, where optical network infrastructure is built by mix and match different physical layer technologies from different vendors. In such a novel context, every equipment and transmission technique at the physical layer impacts the overall network behavior. Hence, methods giving quantitative evaluations of individual merit of physical layer equipment at network level are a firm request during network design phases as well as during network lifetime. Therefore, physical layer awareness in network design and operation is fundamental to fairly assess the potentialities, and exploit the capabilities of different technologies. From this perspective, propagation impairments modeling is essential. In this work propagation impairments in transparent optical networks are summarized, with a special focus on nonlinear effects. The Gaussian Noise model is reviewed, then extended for wideband scenarios. To do so, the impact of polarization mode dispersion on nonlinear interference (NLI) generation is assessed for the first time through simulation, showing its negligible impact on NLI generation. Thanks to this result, the Gaussian Noise model is generalized to assess the impact of space and frequency amplitude variations along the fiber, mainly due to stimulated Raman scattering, on NLI generation. The proposed Generalized GN (GGN) model is experimentally validated on a setup with commercial linecards, compared with other modeling options, and an example of application is shown. Then, network-level power optimization strategies are discussed, and the Locally Optimization Global Optimization (LOGO) approach reviewed. After that, a novel framework of analysis for optical networks that leverages detailed propagation impairment modeling called the Statistical Network Assessment Process (SNAP) is presented. SNAP is motivated by the need of having a general framework to assess the impact of different physical layer technologies on network performance, without relying on rigid optimization approaches, that are not well-suited for technology comparison. Several examples of applications of SNAP are given, including comparisons of transceivers, amplifiers and node technologies. SNAP is also used to highlight topological bottlenecks in progressively loaded network scenarios and to derive possible solutions for them. The final work presented in this thesis is related to the implementation of a vendor agnostic quality of transmission estimator for multi-vendor optical networks developed in the context of the Physical Simulation Environment group of the Telecom Infra Project. The implementation of a module based on the GN model is briefly described, then results of a multi-vendor experimental validation performed in collaboration with Microsoft are shown

Cognitive and Autonomous Software-Defined Open Optical Networks

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An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions