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

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

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 this paper proposing new possible research directions

Application of machine learning techniques to optical communication systems and networks

This TFM reviews the application of machine learning techniques in optical communication systems and networks. In addition, it studies and compares the characteristics of various machine learning methods, such as: support vector machines, logistic regression, decision trees and random forests, to predict the quality of transmission when using optical circuits in wavelength routed optical communication networks. The models developed in this TFM offer better performance than previous proposals, mainly in terms of computing time, making possible its use in online mode even in highly dynamic networks, in addition to being simpler.El presente TFM realiza una revisión de la aplicación de técnicas de aprendizaje automático en los sistemas y redes de comunicaciones ópticas. Además, estudia y compara las características de diversos métodos de aprendizaje automático, tales como: máquinas de vectores soporte, regresión logística, árboles de decisión y bosques aleatorios, para predecir la calidad de la transmisión al emplear circuitos ópticos en redes de comunicaciones ópticas con encaminamiento por longitud de onda. Los modelos desarrollados en el TFM obtienen mejores prestaciones que propuestas anteriores, fundamentalmente en términos de tiempo de cálculo, posibilitando su utilización en modo on-line incluso en redes altamente dinámicas, amén de ser más sencillos.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaMáster en Investigación en Tecnologías de la Información y las Comunicacione

Modulação e conversão de formatos óticos avançados

Doutoramento em Engenharia ElectrotécnicaOver the years, the increased search and exchange of information lead to an increase of traffic intensity in todays optical communication networks. Coherent communications, using the amplitude and phase of the signal, reappears as one of the transmission techniques to increase the spectral efficiency and throughput of optical channels. In this context, this work present a study on format conversion of modulated signals using MZI-SOAs, based exclusively on all- optical techniques through wavelength conversion. This approach, when applied in interconnection nodes between optical networks with different bit rates and modulation formats, allow a better efficiency and scalability of the network. We start with an experimental characterization of the static and dynamic properties of the MZI-SOA. Then, we propose a semi-analytical model to describe the evolution of phase and amplitude at the output of the MZI-SOA. The model’s coefficients are obtained using a multi-objective genetic algorithm. We validate the model experimentally, by exploring the dependency of the optical signal with the operational parameters of the MZI-SOA. We also propose an all-optical technique for the conversion of amplitude modulation signals to a continuous phase modulation format. Finally, we study the potential of MZI-SOAs for the conversion of amplitude signals to QPSK and QAM signals. We show the dependency of the conversion process with the operational parameters deviation from the optimal values. The technique is experimentally validated for QPSK modulation.Nos últimos anos, a crescente procura e troca de informação tem levado ao aumento de tráfego nas redes de comunicação óticas atuais. As comunicações coerentes, com recurso à amplitude e fase do sinal, ressurgem como uma das técnicas de transmissão capazes de aumentar a eficiência espectral e o rendimento dos canais óticos. Nesse âmbito, este trabalho apresenta um estudo sobre a conversão de formatos de modulação de sinais, usando técnicas exclusivamente no domínio ótico, através de conversão de comprimento de onda, com base no MZI-SOA. Esta técnica, aplicada em nós óticos que interligam redes óticas com débitos binários distintos, permite uma maior escalabilidade e eficiência da rede. A tese começa por apresentar uma caracterização experimental detalhada das propriedades estáticas e dinâmicas do MZI-SOA. É depois proposto um modelo semi-analítico que descreve a evolução da amplitude e fase do sinal ótico à saída do MZI-SOA. Os coeficientes do modelo são obtidos recorrendo a um algoritmo genético multiobjectivo. O modelo é validado experimentalmente, explorando a dependência do sinal ótico com os parâmetros operacionais do MZI- SOA. Segue-se a proposta de uma técnica de conversão de formato de modulação de amplitude para modulação de fase contínua. Finalmente, é feito um estudo das potencialidades do MZI-SOA para conversão de formato de modulação de amplitude para modulação QPSK e QAM. Mostra-se a dependência da constelação do sinal com o desvio dos parâmetros operacionais, em torno do valor ótimo. A técnica é validada experimentalmente para modulação QPSK

Advanced optical modulation and format conversion

Tese de Doutoramento em Engenharia Eletrotécnica apresentada à Universidade de Aveiro.Nos últimos anos, a crescente procura e troca de informação tem levado ao aumento de tráfego nas redes de comunicação óticas actuais. As comunicações coerentes, com recurso à amplitude e fase do sinal, ressurgem como uma das técnicas de transmissão capazes de aumentar a eficiência espectral e o rendimento dos canais óticos. Nesse âmbito, este trabalho apresenta um estudo sobre a conversão de formatos de modulação de sinais, usando técnicas exclusivamente no domínio ótico, através de conversão de comprimento de onda, com base no MZI-SOA. Esta técnica, aplicada em nós óticos que interligam redes óticas com débitos binàrios distintos, permite uma maior escalabilidade e eficiência da rede. A tese começa por apresentar uma caracterização experimental detalhada das propriedades estáticas e dinámicas do MZI-SOA. É depois proposto um modelo semi-analítico que descreve a evolução da amplitude e fase do sinal ótico à saída do MZI-SOA. Os coeficientes do modelo são obtidos recorrendo a um algoritmo genético multiobjectivo. O modelo é validado experimentalmente, explorando a dependência do sinal ótico com os parâmetros operacionais do MZISOA. Segue-se a proposta de uma técnica de conversão de formato de modulação de amplitude para modulação de fase contínua. Finalmente, é feito um estudo das potencialidades do MZI-SOA para conversão de formato de modulação de amplitude para modulação QPSK e QAM. Mostra-se a depedência da constelação do sinal com o desvio dos parâmetros operacionais, em torno do valor ótimo. A técnica é validada experimentalmente para modulação QPSK.ABSTRACT: Over the years, the increased search and exchange of information lead to an increase of traffic intensity in todays optical communication networks. Coherent communications, using the amplitude and phase of the signal, reappears as one of the transmission techniques to increase the spectral efficiency and throughput of optical channels. In this context, this work present a study on format conversion of modulated signals using MZI-SOAs, based exclusively on alloptical techniques through wavelength conversion. This approach, when applied in interconnection nodes between optical networks with different bit rates and modulation formats, allow a better efficiency and scalability of the network. We start with an experimental characterization of the static and dynamic properties of the MZI-SOA. Then, we propose a semi-analytical model to describe the evolution of phase and amplitude at the output of the MZI-SOA. The model’s coefficients are obtained using a multi-objective genetic algorithm. We validate the model experimentally, by exploring the dependency of the optical signal with the operational parameters of the MZI-SOA. We also propose an all-optical technique for the conversion of amplitude modulation signals to a continuous phase modulation format. Finally, we study the potential of MZI-SOAs for the conversion of amplitude signals to QPSK and QAM signals. We show the dependency of the conversion process with the operational parameters deviation from the optimal values. The technique is experimentally validated for QPSK modulation.Apoio financeiro da Fundação para a Ciência e Tecnologia — FCT através da bolsa SFRH / PROTEC / 50015 / 2009