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

A tutorial on the EM algorithm for Bayesian networks: application to self-diagnosis of GPON-FTTH networks

International audienceNetwork behavior modelling is a central issue for model-based approaches of self-diagnosis of telecommunication networks. There are two methods to build such models. The model can be built from expert knowledge acquired from network standards and/or the model can be learnt from data generated by network components by data mining algorithms. In a recent work, we proposed a model of architecture and fault propagation for the GPON-FTTH (Gigabit Passive Optical Network-Fiber To The Home) access network. This model is based on a Bayesian network which encodes expert knowledge. This includes dependencies that encode fault propagation and conditional probability distributions that encode the strength of those dependencies. In this paper we consider the problem of automatically tuning the above mentioned probability distributions. This is a parameter estimation problem under missing data conditions that we solve with the Expectation Maximization (EM) algorithm. Conditional probability distributions are learnt from the tremendous amount of alarms generated by an operating GPON-FTTH network during two months in 2015. Self-diagnosis is carried out to analyze the root cause of alarms. The performance of the diagnosis is evaluated with respect to an expert system based on deterministic decision rules currently used by the Internet Access Provider to diagnose network problems

A Tutorial on Machine Learning for Failure Management in Optical Networks

Failure management plays a role of capital importance in optical networks to avoid service disruptions and to satisfy customers' service level agreements. Machine learning (ML) promises to revolutionize the (mostly manual and human-driven) approaches in which failure management in optical networks has been traditionally managed, by introducing automated methods for failure prediction, detection, localization, and identification. This tutorial provides a gentle introduction to some ML techniques that have been recently applied in the field of the optical-network failure management. It then introduces a taxonomy to classify failure-management tasks and discusses possible applications of ML for these failure management tasks. Finally, for a reader interested in more implementative details, we provide a step-by-step description of how to solve a representative example of a practical failure-management task

GPON PLOAMd Message Analysis Using Supervised Neural Networks

This paper discusses the possibility of analyzing the orchestration protocol used in gigabit-capable passive optical networks (GPONs). Considering the fact that a GPON is defined by the International Telecommunication Union Telecommunication sector (ITU-T) as a set of recommendations, implementation across device vendors might exhibit few differences, which complicates analysis of such protocols. Therefore, machine learning techniques are used (e.g., neural networks) to evaluate differences in GPONs among various device vendors. As a result, this paper compares three neural network models based on different types of recurrent cells and discusses their suitability for such analysis

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Producción CientíficaArtificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.Ministerio de Economía, Industria y Competitividad (Project EC2014-53071-C3-2-P, TEC2015-71932-REDT

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

Solución NGN aplicada a la red de telecomunicaciones de la empresa Esmeraldas Santa Rosa S.A

No aplicaCuando la empresa Esmeraldas Santa Rosa decidió tender una red de fibra óptica al interior de la mina de explotación, eran pocos los dispositivos de red conectados, pero con el paso del tiempo se implementó un robusto sistema de video vigilancia que colapsó la red instalada. El sistema no fue capaz de soportar el tráfico de red y se presentaron problemas como latencia alta, perdida de comunicación y saturación. Con la migración de la infraestructura a redes de Nueva Generación se garantizó la solución de las fallas y problemas de red, modernizando los equipos y la infraestructura. Para ello fue necesario realizar un levantamiento de información para validar la red y los dispositivos a migrar, conocer los operadores de la zona que ofrecieran el servicio de internet y con capacidad de establecer un canal MPLS con la sede de Bogotá y diseñar una red acorde a las exigencias de las redes NGN. En el diseño se optó por implementar una red GPON por las características que esta ofrece, como tener acceso a cada uno de los dispositivos instalados, por ejemplo. Se realizaron pruebas de conectividad garantizando el éxito de la migración.When the Esmeraldas Santa Rosa company decided to lay a fiber optic network inside the exploitation mine, few network devices were connected, but with the passage of time a robust video surveillance system was implemented that collapsed the installed network. The system was not able to support the network traffic and there were problems such as high latency, loss of communication and saturation. With the migration of the infrastructure to New Generation networks, the solution of failures and network problems was guaranteed, a GPON system was implemented inside the mine, modernizing the equipment and infrastructure. For this, it was necessary to carry out an information survey to validate the network and the devices to be migrated, meet the operators in the area that offer internet service and with the ability to establish an MPLS channel with the Bogotá headquarters and design a network accordingly. to the demands of NGN networks. In the design, it was decided to implement a GPON network due to the characteristics it offers, such as having access to each of the installed devices, for example. Connectivity tests were carried out guaranteeing the success of the migration

Telecommunication Systems

This book is based on both industrial and academic research efforts in which a number of recent advancements and rare insights into telecommunication systems are well presented. The volume is organized into four parts: "Telecommunication Protocol, Optimization, and Security Frameworks", "Next-Generation Optical Access Technologies", "Convergence of Wireless-Optical Networks" and "Advanced Relay and Antenna Systems for Smart Networks." Chapters within these parts are self-contained and cross-referenced to facilitate further study