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

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

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