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Digital Twins (DT) are needed in elastic optical networks to enable further advance and pave the way towards autonomous networks. Dynamical models self-adjusting with their physical counterpart have to maintain low computational complexity and guarantee good accuracy, regardless of the network topology and the technology supporting the optical lightpath. In this work, we propose data-driven models based on cascaded learning for single carrier signal as well as digital subcarrier multiplexing signals. Firstly, we explore the potentials of such a DT by proposing an algorithm for lightpath provisioning. In addition, the performances of the proposed models are compared with the ones of analytical models. Secondly, we experimentally evaluate its accuracy to predict the impact of optical filtering penalties. The simulations show an overall good accuracy for different signal formats and demonstrate their viability for lightpath provisioning. Similarly, the experimental results demonstrate the feasibility of implementing such models to estimate the quality of transmission for digital subcarrier multiplexed signals., The research leading to these results has received funding from the European Community through the MSCA MENTOR (G.A. 956713), the European Union's Horizon Europe research and innovation programme SEASON (G.A. 101096120), the MICINN IBON (PID2020-114135RB-I00) and from the ICREA Institution., Peer Reviewed, Postprint (published version)

© 2024 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., Time-domain digital twin models for single carrier and DSCM signals are developed that propagate features to estimate the impact of filter penalties on the BER. Results show remarkable accuracy, which is used for lightpath provisioning., The research leading to these results has received funding from the European Community through the MSCA MENTOR (G.A. 956713), the European Union's Horizon Europe research and innovation programme SEASON (G.A. 101096120), the MICINN IBON (PID2020-114135RB-I00) and from the ICREA Institution., Peer Reviewed, Postprint (author's final draft)

The development of digital twins to represent the optical transport network might enable multiple applications for network operation, including automation and fault management. In this work, we propose a deep-learning-based digital twin for the optical time domain, named OCATA. OCATA is based on the concatenation of deep neural network (DNN) modeling of optical links and nodes, which facilitates representing lightpaths. The DNNs model linear and nonlinear noise, as well as optical filtering. Additional DNN-based models are proposed to extract useful lightpath metrics, such as lightpath length, number of optical links, and nonlinear fiber parameters. OCATA exhibits low complexity, thus making it ideal for real-time applications. Illustrative results for the application of OCATA to disaggregated and mixed disaggregated-proprietary optical network scenarios reveal remarkable accuracy., The research leading to these results has received funding from the MSCA REAL-NET (G.A. 813144), the H2020 B5G-OPEN (G.A. 101016663), and the AEI IBON (PID2020-114135RB-I00) projects and from the ICREA Institution., Peer Reviewed, Postprint (author's final draft)

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 Reviewed, Postprint (author's final draft)

Network automation promises to reduce costs while guaranteeing the required performance; this is paramount when dealing with the forecasted highly dynamic traffic that will be generated by new 5G/6G applications. In optical networks, autonomous lightpath operation entails that the optical receiver can identify the configuration of a received optical signal without necessarily being configured from the network controller. This provides relief for the network controller from real-time operation, and it can simplify the operation in multi-domain scenarios, where an optical connection spans across more than one domain. Consequently, in this work, we propose a blind and low complex modulation format (MF) and symbol rate (SR) identification algorithm. The algorithm is based on studying the effects of decoding an optical signal with different MFs and SRs. Extensive MATLAB-based simulations have been carried out which consider a coherent wavelength division multiplexed system based on 32 and 64 quadrature amplitude modulated signals at up to 96 GBd, thus enabling bit rates of up to 800 Gb/s/channel. The results show remarkable identification accuracy in the presence of linear and nonlinear noise for a wide range of feasible configurations., This research was funded by European Commission through the H2020 MSCA REAL-NET (G.A. 813144) and the B5G-OPEN (G.A. 101016663) projects, by the AEI through the IBON project (PID2020-114135RB-I00), and by ICREA institution., Peer Reviewed, Postprint (published version)

Judo is an Olympic combat sport that is divided into male and female weight categories. Each weight category implies significant differences in technical and strategic dimensions, as well as physiological, performance, and body composition among competitors. This individual variability can widely mark options and predominance of biomechanical actions inherent to its own movements. This study aimed to determine the technical actions predominance used by judokas in the international competitions final stages scoring for the Olympic ranking. We observed 285 judo matches, in several weight categories for men and women, played in the final stages of six scoring international competitions for the Olympic ranking in Tokyo 2021. An observation system was used according to the biomechanical classification of judo techniques proposed by Sacripanti. In Nage-Waza combat, there was a predominance of lever techniques over couple techniques. In Ne-Waza there was a predominance of pressure techniques, followed by venous breathing block and dislocation of the elbow joint. There were also differences between genders and significant associations by weight category in the technical options in combat, as well as according to the combat phase., El judo es un deporte de combate olímpico que se divide en categorías de peso masculino y femenino. Cada categoría de peso implica diferencias significativas en las dimensiones técnicas y estratégicas, así como fisiológicas, de rendimiento y de composición corporal entre los competidores. Esta variabilidad individual puede marcar ampliamente opciones y predominio de acciones biomecánicas inherentes a sus propios movimientos. Este estudio tuvo como objetivo determinar el predominio de las acciones técnicas utilizadas por los judokas en las etapas finales de las competencias internacionales puntuables para el ranking olímpico. Observamos 285 combates de judo, en varias categorías de peso para hombres y mujeres, disputados en las etapas finales de seis competencias internacionales puntuables para el ranking olímpico en Tokio 2021. Se utilizó un sistema de observación de acuerdo con la clasificación biomecánica de técnicas de judo propuesta por Sacripanti. En el combate Nage-Waza predominaban las técnicas de palanca sobre las técnicas de binario. En Ne-Waza predominaron las técnicas de presión, seguidas del bloqueo respiratorio venoso y la luxación de la articulación del codo. También hubo diferencias entre géneros y asociaciones significativas por categoría de peso en las opciones técnicas en combate, así como según la fase de combate.

© [2022 Optical Society of America]. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved., Optical network automation requires accurate physical layer models, not only for provisioning but also for real-time analysis. In particular, In-Phase (I) and Quadrature (Q) constellation analysis enables deep understanding of the characteristics of optical connections (lightpaths), e.g., their length. In this paper, we present methods for modeling lightpaths based on deep learning. Specifically, we propose using autoencoders (AE) and deep neural networks (DNN). Models are trained and composed in a sandbox domain with the information received from the network controller and sent to the node agent that uses them to compare the features extracted from the received signal and the expected features returned by the models. We investigate two different use cases for lightpath analysis focused on lightpath length and optical signal power. The results show a remarkable accuracy for the lightpath modelling and length prediction and a noticeable performance of the AEs for unsupervised IQ constellation features extraction and relevance analysis. © 2021 Optical Society of America, European Commission (H2020 B5G-OPEN (G.A. 101016663), MSCA REAL-NET project (G.A. 813144)); Agencia Estatal de Investigación (IBON (PID2020-114135RB-I00)); Institució Catalana de Recerca i Estudis Avançats, Peer Reviewed, Postprint (published version)

(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 algor, (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 e, Postprint (published version)

A lightweight optical constellations modeling method based on concatenating ANNs is proposed. Statistical validation of the reproduced constellations is shown. The method accelerates data generation and facilitates detecting (un)intentioned misconfigurations, among others., This work has been partially supported by the EC through the MSC REAL-NET project (G.A. 813144), by the AEI/FEDER through the TWINS project (TEC2017-90097-R), and by the ICREA institution., Peer Reviewed, Postprint (author's final draft)

A method for IQ constellation analysis based on Autoencoders is proposed. Exhaustive numerical results show accurate physical metric prediction and large data compression, while providing useful model explainability., This work has been partially supported by the EC through the MSC REAL-NET project (G.A. 813144), by the AEI/FEDER through the TWINS project (TEC2017- 90097-R), and by the ICREA institution., Peer Reviewed, Postprint (author's final draft)

The massive deployment of 5G and beyond will require high capacity and low latency connectivity services, so network operators will have either to overprovision capacity in their transport networks or to upgrade the optical network controllers to make decisions nearly in real time; both solutions entail high capital and operational expenditures. A different approach could be to move the decision making toward the nodes and subsystems, so they can adapt dynamically the capacity to the actual needs and thus reduce operational costs in terms of energy consumption. To achieve this, several technological challenges need to be addressed. In this paper, we focus on the autonomous operation of Digital Subcarrier Multiplexing (DSCM) systems, which enable the transmission of multiple and independent subcarriers (SC). Herein, we present several solutions enabling the autonomous DSCM operation, including: i) SC quality of transmission estimation; ii) autonomous SC operation at the transmitter side and blind SC configuration recognition at the receiver side; and iii) intent-based capacity management implemented through Reinforcement Learning. We provide useful guidelines for the application of autonomous SC management supported by the extensive results presented., The research leading to these results has received funding from the European Community's through the MSCA REAL-NET project (G.A. 813144), by the AEI/FEDER through the TWINS project (TEC2017-90097-R), and by the ICREA institution., Peer Reviewed, Postprint (author's final draft)