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

Analytical Models and Artificial Intelligence for Open and Partially Disaggregated Optical Networks

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Developing coherent optical wavelength conversion systems for reconfigurable photonic networks

In future optical networks that employ wavelength division multiplexing (WDM), the use of optical switching technologies on a burst or packet level, combined with advanced modulation formats would achieve greater spectral efficiency and utilize the existing bandwidth more efficiently. All-optical wavelength converters are expected to be one of the key components in these broadband networks. They can be used at the network nodes to avoid contention and to dynamically allocate wavelengths to ensure optimum use of fiber bandwidth. In this work, a reconfigurable wavelength converter comprising of a Semiconductor Optical Amplifier (SOA) as the nonlinear element and a fast-switching sampled grating distributed Bragg reflector (SG-DBR) tunable laser as one of the pumps is developed. The wavelength conversion of 12.5-Gbaud quadrature phase shift keying (QPSK) and Pol-Mul QPSK signals with switching time of tens of nanoseconds is experimentally achieved. Although the tunable DBR lasers can achieve ns tuning time, they present relatively large phase noise. The phase noise transfer from the pump to the converted signal can have a deleterious effect on signal quality and cause a performance penalty with phase modulated signals. To overcome the phase noise transfer issue, a wavelength converter using tunable dual-correlated pumps provided by the combination of a single-section quantum dash passively mode-locked laser (QD-PMLL) and a programmable tunable optical filter is designed and the wavelength conversion of QPSK and 16-quadrature amplitude modulation (16-QAM) signals at 12.5 GBaud is experimentally investigated. Nonlinear distortion of the wavelength converted signal caused by gain saturation effects in the SOA can significantly degrade the signal quality and cause difficulties for the practical wavelength conversion of sig nal data with advanced modulation formats. In this work, the machine learning clustering based nonlinearity compensation method is proposed to improve the tolerance to nonlinear distortion in an SOA based wavelength conversion system with 16 QAM and 64 QAM signals

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

Next-generation High-Capacity Communications with High Flexibility, Efficiency, and Reliability

The objective of this dissertation is to address the flexibility, efficiency and reliability in high-capacity heterogeneous communication systems. We will experimentally investigate the shaping techniques, and further extend them to more diverse and complicated scenarios, which result in more flexible systems. The scenarios include 1) entropy allocation scheme under uneven frequency response for multi-carrier system, 2) fiber-free space optics link using unipolar pairwise distribution, and 3) flexible rate passive optical network with a wide range of received optical powers. Next, we perform efficiency analysis in inter-data center and long-haul communications. We will characterize the impact of the laser linewidth, jitter tones, and the flicker noise on coherent systems with different baud rates and fiber lengths through theoretical analysis, simulation, and experimental validation. The trade-off analysis indicates the importance of setting up frequency noise power spectral density masks to qualify the transceiver laser design. Besides efficiency analysis, we will also work on efficient system architecture and algorithm design. We investigate the combined impact of various hardware impairments using proposed simplified DSP schemes in beyond 800G self-homodyne coherent system. The proposed scheme is very promising for next-generation intra-data center applications. On the other hand, to improve the data efficiency of the nonlinearity correction algorithm in broadband communication systems, we leverage the semi-supervised method and Lasso. Experimental results validate that Lasso can reduce the required pilot symbol number by exploiting the sparsity of the tap coefficients. Semi-supervised method can further enhance the system performance without introducing additional overhead. Last but not least, regarding reliability, we propose and experimentally demonstrate an ultra-reliable integrated millimeter wave and free space optics analog radio over fiber system with algorithm design. The multiple-spectra operation shows superior performance in reliability and sensitivity compared to the conventional systems, even in extreme weather conditions and strong burst interference.Ph.D

Optics for AI and AI for Optics

Artificial intelligence is deeply involved in our daily lives via reinforcing the digital transformation of modern economies and infrastructure. It relies on powerful computing clusters, which face bottlenecks of power consumption for both data transmission and intensive computing. Meanwhile, optics (especially optical communications, which underpin today’s telecommunications) is penetrating short-reach connections down to the chip level, thus meeting with AI technology and creating numerous opportunities. This book is about the marriage of optics and AI and how each part can benefit from the other. Optics facilitates on-chip neural networks based on fast optical computing and energy-efficient interconnects and communications. On the other hand, AI enables efficient tools to address the challenges of today’s optical communication networks, which behave in an increasingly complex manner. The book collects contributions from pioneering researchers from both academy and industry to discuss the challenges and solutions in each of the respective fields

Design, monitoring and performance evaluation of high capacity optical networks

Premi Extraordinari de Doctorat, promoció 2018-2019. Àmbit de les TICInternet traffic is expected to keep increasing exponentially due to the emergence of a vast number of innovative online services and applications. Optical networks, which are the cornerstone of the underlying Internet infrastructure, have been continuously evolving to carry the ever-increasing traffic in a more flexible, cost-effective, and intelligent way. Having these three targets in mind, this PhD thesis focuses on two general areas for the performance improvement and the evolution of optical networks: i) introducing further cognition to the optical layer, and ii) introducing new networking solutions revolutionizing the optical transport infrastructure. In the first part, we present novel failure detection and identification solutions in the optical layer utilizing the optical spectrum traces captured by cost-effective coarse-granular Optical Spectrum Analyzers (OSA). We demonstrate the effectiveness of the developed solutions for detecting and identifying filter-related failures in the context of Spectrum-Switched Optical Networks (SSON), as well as transmitter-related laser failures in Filter-less Optical Networks (FON). In addition, at the subsystem level we propose an Autonomic Transmission Agent (ATA), which triggers local or remote transceiver reconfiguration by predicting Bit-Error-Rate (BER) degradation by monitoring State-of-Polarization (SOP) data obtained by coherent receivers. I have developed solutions to push further the performance of the currently deployed optical networks through reducing the margins and introducing intelligence to better manage their resources. However, it is expected that the spectral efficiency of the current standard Single-Mode Fiber (SMF) based optical network approaches the Shannon capacity limits in the near future, and therefore, a new paradigm is required to keep with the pace of the current huge traffic increase. In this regard, Space Division Multiplexing (SDM) is proposed as the ultimate solution to address the looming capacity crunch with a reduced cost-per-bit delivered to the end-users. I devote the second part of this thesis to investigate different flavors of SDM based optical networks with the aim of finding the best compromise for the realization of a spectrally and spatially flexible optical network. SDM-based optical networks can be deployed over various types of transmission media. Additionally, due to the extra dimension (i.e., space) introduced in SDM networks, optical switching nodes can support wavelength granularity, space granularity, or a combination of both. In this thesis, we evaluate the impact of various spectral and spatial switching granularities on the performance of SDM-based optical networks serving different profiles of traffic with the aim of understanding the impact of switching constraints on the overall network performance. In this regard, we consider two different generations of wavelength selective switches (WSS) to reflect the technology limitations on the performance of SDM networks. In addition, we present different designs of colorless direction-less, and Colorless Directionless Contention-less (CDC) Reconfigurable Optical Add/Drop Multiplexers (ROADM) realizing SDM switching schemes and compare their performance in terms of complexity and implementation cost. Furthermore, with the aim of revealing the benefits and drawbacks of SDM networks over different types of transmission media, we preset a QoT-aware network planning toolbox and perform comparative performance analysis among SDM network based on various types of transmission media. We also analyze the power consumption of Multiple-Input Multiple-Output (MIMO) Digital Signal Processing (DSP) units of transceivers operating over three different types of transmission media. The results obtained in the second part of the thesis provide a comprehensive outlook to different realizations of SDM-based optical networks and showcases the benefits and drawbacks of different SDM realizations.Se espera que el tráfico de Internet siga aumentando exponencialmente debido a la continua aparición de gran cantidad de aplicaciones innovadoras. Las redes ópticas, que son la piedra angular de la infraestructura de Internet, han evolucionado continuamente para transportar el tráfico cada vez mayor de una manera más flexible, rentable e inteligente. Teniendo en cuenta estos tres objetivos, esta tesis doctoral se centra en dos áreas cruciales para la mejora del rendimiento y la evolución de las redes ópticas: i) introducción de funcionalidades cognitivas en la capa óptica, y ii) introducción de nuevas estructuras de red que revolucionarán el transporte óptico. En la primera parte, se presentan soluciones novedosas de detección e identificación de fallos en la capa óptica que utilizan trazas de espectro óptico obtenidas mediante analizadores de espectros ópticos (OSA) de baja resolución (y por tanto de coste reducido). Se demuestra la efectividad de las soluciones desarrolladas para detectar e identificar fallos derivados del filtrado imperfecto en las redes ópticas de conmutación de espectro (SSON), así como fallos relacionados con el láser transmisor en redes ópticas sin filtro (FON). Además, a nivel de subsistema, se propone un Agente de Transmisión Autónomo (ATA), que activa la reconfiguración del transceptor local o remoto al predecir la degradación de la Tasa de Error por Bits (BER), monitorizando el Estado de Polarización (SOP) de la señal recibida en un receptor coherente. Se han desarrollado soluciones para incrementar el rendimiento de las redes ópticas mediante la reducción de los márgenes y la introducción de inteligencia en la administración de los recursos de la red. Sin embargo, se espera que la eficiencia espectral de las redes ópticas basadas en fibras monomodo (SMF) se acerque al límite de capacidad de Shannon en un futuro próximo, y por tanto, se requiere un nuevo paradigma que permita mantener el crecimiento necesario para soportar el futuro aumento del tráfico. En este sentido, se propone el Multiplexado por División Espacial (SDM) como la solución que permita la continua reducción del coste por bit transmitido ante ése esperado crecimiento del tráfico. En la segunda parte de esta tesis se investigan diferentes tipos de redes ópticas basadas en SDM con el objetivo de encontrar soluciones para la realización de redes ópticas espectral y espacialmente flexibles. Las redes ópticas basadas en SDM se pueden implementar utilizando diversos tipos de medios de transmisión. Además, debido a la dimensión adicional (el espacio) introducida en las redes SDM, los nodos de conmutación óptica pueden conmutar longitudes de onda, fibras o una combinación de ambas. Se evalúa el impacto de la conmutación espectral y espacial en el rendimiento de las redes SDM bajo diferentes perfiles de tráfico ofrecido, con el objetivo de comprender el impacto de las restricciones de conmutación en el rendimiento de la red. En este sentido, se consideran dos generaciones diferentes de conmutadores selectivos de longitud de onda (WSS) para reflejar las limitaciones de la tecnología en el rendimiento de las redes SDM. Además, se presentan diferentes diseños de ROADM, independientes de la longitud de onda, de la dirección, y sin contención (CDC) utilizados para la conmutación SDM, y se compara su rendimiento en términos de complejidad y coste. Además, con el objetivo de cuantificar los beneficios e inconvenientes de las redes SDM, se ha generado una herramienta de planificación de red que prevé la QoT usando diferentes tipos de fibras. También se analiza el consumo de energía de las unidades DSP de los transceptores MIMO operando en redes SDM con tres tipos diferentes de medios de transmisión. Los resultados obtenidos en esta segunda parte de la tesis proporcionan una perspectiva integral de las redes SDM y muestran los beneficios e inconvenientes de sus diferentes implementacionesAward-winningPostprint (published version