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

Design, monitoring and performance evaluation of high capacity optical networks

Internet 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 implementacione

Free space intra-datacenter interconnects based on 2D optical beam steering enabled by photonic integrated circuits

Data centers are continuously growing in scale and can contain more than one million servers spreading across thousands of racks; requiring a large-scale switching network to provide broadband and reconfigurable interconnections of low latency. Traditional data center network architectures, through the use of electrical packet switches in a multi-tier topology, has fundamental weaknesses such as oversubscription and cabling complexity. Wireless intra-data center interconnection solutions have been proposed to deal with the cabling problem and can simultaneously address the over-provisioning problem by offering efficient topology re-configurability. In this work we introduce a novel free space optical interconnect solution for intra-data center networks that utilizes 2D optical beam steering for the transmitter, and high bandwidth wide-area photodiode arrays for the receiver. This new breed of free space optical interconnects can be developed on a photonic integrated circuit; offering ns switching at sub-µW consumption. The proposed interconnects together with a networking architecture that is suitable for utilizing those devices could support next generation intra-data center networks, fulfilling the requirements of seamless operation, high connectivity, and agility in terms of the reconfiguration time.Peer ReviewedPostprint (published version

Learning from the optical spectrum: failure detection and identification [Invited]

© 2018 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 worksThe availability of coarse-resolution cost-effective Optical Spectrum Analyzers (OSA) allows its widespread deployment in operators’ networks. In this paper, we explore several machine learning approaches for soft-failure detection, identification and localization that take advantage of OSAs. In particular, we present three different solutions for the two most common filter-related soft-failures; filter shift and tight filtering which noticeably deform the expected shape of the optical spectrum. However, filter cascading is a key challenge as it affects the shape of the optical spectrum similarly to tight filtering; the approaches are specifically designed to avoid the misclassification of properly operating signals when normal filter cascading effects are present. The proposed solutions are: $i) multi-classifier$ approach, which uses features extracted directly from the optical spectrum, $ii) single-classifier$ approach, which uses pre-processed features to compensate for filter cascading, and $iii) residual-based$ approach, which uses a residual signal computed from subtracting the acquired single by OSAs from an expected signal synthetically generated. Extensive numerical results are ultimately presented to compare the performance of the proposed approaches in terms of accuracy and robustness.Peer ReviewedPostprint (author's final draft

Optical signal tracking for robust PAM4 deployment in filterless metro network scenarios

© 2019 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.Highly accurate and reliable optical signal tracking is proposed that estimates sub-GHz laser drift failures by analyzing spectra acquired by cost-effective coarse-granular OSAs. Its application on PAM4 systems in filterless metro networks brings added robustness.Peer ReviewedPostprint (author's final draft

Learning life cycle to speed up autonomic optical transmission and networking adoption

Autonomic optical transmission and networking requires machine learning (ML) models to be trained with large datasets. However, the availability of enough real data to produce accurate ML models is rarely ensured since new optical equipment and techniques are continuously being deployed in the network. One option is to generate data from simulations and lab experiments, but such data could not cover the whole features space and would translate into inaccuracies in the ML models. In this paper, we propose an ML-based algorithm life cycle to facilitate ML deployment in real operator networks. The dataset for ML training can be initially populated based on the results from simulations and lab experiments. Once ML models are generated, ML retraining can be performed after inaccuracies are detected to improve their precision. Illustrative numerical results show the benefits of the proposed learning cycle for general use cases. In addition, two specific use cases are proposed and demonstrated that implement different learning strategies: (i) a two-phase strategy performing out-of-field training using data from simulations and lab experiments with generic equipment, followed by an in-field adaptation to support heterogeneous equipment (the accuracy of this strategy is shown for a use case of failure detection and identification), and (ii) in-field retraining, where ML models are retrained after detecting model inaccuracies. Different approaches are analyzed and evaluated for a use case of autonomic transmission, where results show the significant benefits of collective learning.Peer ReviewedPostprint (published version

Predictive autonomic transmission for low-cost low-margin metro optical networks

Low-cost low-margin implementation plays an essential role in upgrading optical metro networks required for future 5G ecosystem. In this regard, low-resolution analog-to-digital converters can be used in coherent optical transponders to reduce cost and power consumption. However, the resulting transmission systems become more sensitive to physical layer fluctuations like the events caused by fiber stressing. Such fluctuations might have a strong impact on the quality of transmission (QoT) of the signals. To guarantee robust operation, soft decision forward error correction (FEC) techniques are required to guarantee zero post-FEC bit error rate (BER) transmission, which could increase the power consumption of the receiver and thus operational expenses. In this paper, we aim at minimizing power consumption while keeping zero post-FEC errors by means of a predictive autonomic transmission agent (ATA) based on machine learning. We present a sophisticated ATA model that, taking advantage of real-time monitoring of state of polarization traces and the corresponding pre-FEC BER, predicts the right FEC configuration for short-term operation, thus requiring minimum power consumption. In addition, we propose a complementary long-term prediction of excessive pre-FEC BER to enable remote reconfiguration at the transmitter side through the network controller. A set of experimental measurements is used to train and validate the proposed ATA system. Exhaustive numerical analysis allows concluding that ATA based on artificial neural network predictors achieves the maximum QoT robustness with 80% power consumption reductions compared to static FEC configuration.The research leading to these results has received funding from the European Commission for the H2020-ICT-2016-2 METRO-HAUL project (G.A. 761727), from the AEI/FEDER TWINS project (TEC2017-90097-R), and from the Catalan Institution for Research and Advanced Studies (ICREA).Peer ReviewedPostprint (author's final draft

Experimental validation of deep learning-based models for optical time domain analysis

Optical constellations generated by Deep Learning models trained with datasets generated through simulation are compared to experimentally collected ones. The obtained high accuracy enables its application for optical time domain analysis in complex network scenarios.The research leading to these results has received funding from the European Commission through the MSCA MENTOR (G.A. 956713), the MSCA REAL-NET (G.A. 813144), and the H2020 B5G-OPEN (G.A. 101016663) projects, the AEI through the IBON (PID2020-114135RB-I00) project, and by the ICREA institution.Peer ReviewedPostprint (author's final draft

Distributed architecture supporting intelligent optical measurement aggregation and streaming event telemetry

A distributed telemetry system integrating optical measurement and event data collection is demonstrated. Measurements of optical spectra from Nokia Bell Labs, of optical transponders from ADVA and SDN controller events from CTTC will be showcased.The research leading to these results has received funding from the H2020 B5G-OPEN (G.A. 101016663) and the MICINN IBON (PID2020-114135RB-I00) projects and from the ICREA Institution.Peer ReviewedPostprint (author's final draft

Demonstration of latency-aware 5G network slicing on optical metro networks

The H2020 METRO-HAUL European project has architected a latency-aware, cost-effective, agile, and programmable optical metro network. This includes the design of semi-disaggregated metro nodes with compute and storage capabilities, which interface effectively with both 5G access and multi-Tbit/s elastic optical networks in the core. In this paper, we report the automated deployment of 5G services, in particular, a public safety video surveillance use case employing low-latency object detection and tracking using on-camera and on-the-edge analytics. The demonstration features flexible deployment of network slice instances, implemented in terms of ETSI NFV Network Services. We summarize the key findings in a detailed analysis of end-to-end quality of service, service setup time, and soft-failure detection time. The results show that the round-trip-time over an 80 km link is under 800 µs and the service deployment time under 180 seconds.Horizon 2020 Framework Programme (761727); Bundesministerium für Bildung und Forschung (16KIS0979K).Peer ReviewedArticle signat per 25 autors/es: B. Shariati, Fraunhofer HHI, Berlin, Germany / L. Velasco, Universitat Politècnica de Catalunya, Barcelona, Spain / J.-J. Pedreno-Manresa, ADVA, Munich, Germany / A. Dochhan, ADVA, Munich, Germany / R. Casellas, Centre Tecnològic Telecomunicacions Catalunya, Castelldefels, Spain / A. Muqaddas, University of Bristol, Bristol, UK / O. Gonzalez de Dios, Telefónica, Madrid, Spain / L. Luque Canto, Telefónica, Madrid, Spain / B. Lent, Qognify GmbH, Bruchsal, Germany / J. E. Lopez de Vergara, Naudit HPCN, Madrid, Spain / S. Lopez-Buedo, Naudit HPCN, Madrid, Spain / F. Moreno, Universidad Politécnica de Cartagena, Cartagena, Spain / P. Pavon, Universidad Politécnica de Cartagena, Cartagena, Spain / M. Ruiz, Universitat Politècnica de Catalunya, Barcelona, Spain / S. K. Patri, ADVA, Munich, Germany / A. Giorgetti, CNIT, Pisa, Italy / F. Cugini, CNIT, Pisa, Italy / A. Sgambelluri, CNIT, Pisa, Italy / R. Nejabati, University of Bristol, Bristol, UK / D. Simeonidou, University of Bristol, Bristol, UK / R.-P. Braun, Deutsche Telekom, Germany / A. Autenrieth, ADVA, Munich, Germany / J.-P. Elbers, ADVA, Munich, Germany / J. K. Fischer, Fraunhofer HHI, Berlin, Germany / R. Freund, Fraunhofer HHI, Berlin, GermanyPostprint (author's final draft