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

Analog radio over fiber solutions for multi-band 5g systems

This study presents radio over fiber (RoF) solutions for the fifth-generation (5G) of wireless networks. After the state of the art and a technical background review, four main contributions are reported. The first one is proposing and investigating a RoF technique based on a dual-drive Mach-Zehnder modulator (DD-MZM) for multi-band mobile fronthauls, in which two radiofrequency (RF) signals in the predicted 5G bands individually feed an arm of the optical modulator. Experimental results demonstrate the approach enhances the RF interference mitigation and can prevail over traditional methods. The second contribution comprises the integration of a 5G transceiver, previously developed by our group, in a passive optical network (PON) using RoF technology and wavelength division multiplexing (WDM) overlay. The proposed architecture innovates by employing DD-MZM and enables to simultaneously transport baseband and 5G candidate RF signals in the same PON infrastructure. The proof-of-concept includes the transmission of a generalized frequency division multiplexing (GFDM) signal generated by the 5G transceiver in the 700 MHz band, a 26 GHz digitally modulated signal as a millimeter-waves 5G band, and a baseband signal from an gigabit PON (GPON). Experimental results demonstrate the 5G transceiver digital performance when using RoF technology for distributing the GFDM signal, as well as Gbit/s throughput at 26 GHz. The third contribution is the implementation of a flexible-waveform and multi-application fiber-wireless (FiWi) system toward 5G. Such system includes the FiWi transmission of the GFDM and filtered orthogonal frequency division multiplexing (F-OFDM) signals at 788 MHz, toward long-range cells for remote or rural mobile access, as well as the recently launched 5G NR standard in microwave and mm-waves, aiming enhanced mobile broadband indoor and outdoor applications. Digital signal processing (DSP) is used for selecting the waveform and linearizing the RoF link. Experimental results demonstrate the suitability of the proposed solution to address 5G scenarios and requirements, besides the applicability of using existent fiber-to-the-home (FTTH) networks from Internet service providers for implementing 5G systems. Finally, the fourth contribution is the implementation of a multi-band 5G NR system with photonic-assisted RF amplification (PAA). The approach takes advantage of a novel PAA technique, based on RoF technology and four-wave mixing effect, that allows straightforward integration to the transport networks. Experimental results demonstrate iv uniform and stable 15 dB wideband gain for Long Term Evolution (LTE) and three 5G signals, distributed in the frequency range from 780 MHz to 26 GHz and coexisting in the mobile fronthaul. The obtained digital performance has efficiently met the Third-Generation Partnership Project (3GPP) requirements, demonstrating the applicability of the proposed approach for using fiber-optic links to distribute and jointly amplify LTE and 5G signals in the optical domain.Agência 1Este trabalho apresenta soluções de rádio sobre fibra (RoF) para aplicações em redes sem fio de quinta geração (5G), e inclui quatro contribuições principais. A primeira delas refere-se à proposta e investigação de uma técnica de RoF baseada no modulador eletroóptico de braço duplo, dual-drive Mach-Zehnder (DD-MZM), para a transmissão simultânea de sinais de radiofrequência (RF) em bandas previstas para redes 5G. Resultados experimentais demonstram que o uso do DD-MZM favorece a ausência de interferência entre os sinais de RF transmitidos. A segunda contribuição trata da integração de um transceptor de RF, desenvolvido para aplicações 5G e apto a prover a forma de onda conhecida como generalized frequency division multiplexing (GFDM), em uma rede óptica passiva (PON) ao utilizar RoF e multiplexação por divisão de comprimento de onda (WDM). A arquitetura proposta permite transportar, na mesma infraestrutura de rede, sinais em banda base e de radiofrequência nas faixas do espectro candidatas para 5G. A prova de conceito inclui a distribuição conjunta de três tipos de sinais: um sinal GFDM na banda de 700 MHz, proveniente do transceptor desenvolvido; um sinal digital na frequência de 26 GHz, assumindo a faixa de ondas milimétricas; sinais em banda base provenientes de uma PON dedicada ao serviço de Internet. Resultados experimentais demonstram o desempenho do transceptor de RF ao utilizar a referida arquitetura para distribuir sinais GFDM, além de taxas de transmissão de dados da ordem de Gbit/s na faixa de 26 GHz. A terceira contribuição corresponde à implementação de um sistema fibra/rádio potencial para redes 5G, operando inclusive com o padrão ―5G New Radio (5G NR)‖ nas faixas de micro-ondas e ondas milimétricas. Tal sistema é capaz de prover macro células na banda de 700 MHz para aplicações de longo alcance e/ou rurais, utilizando sinais GFDM ou filtered orthogonal frequency division multiplexing (F-OFDM), assim como femto células na banda de 26 GHz, destinada a altas taxas de transmissão de dados para comunicações de curto alcance. Resultados experimentais demonstram a aplicabilidade da solução proposta para redes 5G, além da viabilidade de utilizar redes ópticas pertencentes a provedores de Internet para favorecer sistemas de nova geração. Por fim, a quarta contribuição trata da implementação de um sistema 5G NR multibanda, assistido por amplificação de RF no domínio óptico. Esse sistema faz uso de um novo método de amplificação, baseado no efeito não linear da mistura de quatro ondas, que vi permite integração direta em redes de transporte envolvendo rádio sobre fibra. Resultados experimentais demonstram ganho de RF igual a 15 dB em uma ampla faixa de frequências (700 MHz até 26 GHz), atendendo simultaneamente tecnologias de quarta e quinta geração. O desempenho digital obtido atendeu aos requisitos estabelecidos pela 3GPP (Third-Generation Partnership Project), indicando a aplicabilidade da solução em questão para distribuir e conjuntamente amplificar sinais de RF em enlaces de fibra óptica

Optimizing total cost of ownership (TCO) for 5G multi-tenant mobile backhaul (MBH) optical transport networks

Legacy network elements are reaching end-of-life and packet-based transport networks are not efficiently optimized. In particular, high density cell architecture in future 5G networks will face big technical and financial challenges due to avalanche of traffic volume and massive growth in connected devices. Raising density and ever-increasing traffic demand within future 5G Heterogeneous Networks (HetNets) will result in huge deployment, expansion and operating costs for upcoming Mobile BackHaul (MBH) networks with flat revenue generation. Thus, the goal of this dissertation is to provide an efficient physical network planning mechanism and an optimized resource engineering tool in order to reduce the Total Cost of Ownership (TCO) and increase the generated revenues. This will help Service Providers (SPs) and Mobile Network Operators (MNOs) to improve their network scalability and maintain positive Project Profit Margins (PPM). In order to meet this goal, three key issues are required to be addressed in our framework and are summarized as follows: i) how to design and migrate to a scalable and reliable MBH network in an optimal cost?, ii) how to control the deployment and activation of the network resources in such MBH based on required traffic demand in an efficient and cost-effective way?, and iii) how to enhance the resource sharing in such network and maximize the profit margins in an efficient way? As part of our contributions to address the first issue highlighted above and to plan the MBH with reduced network TCO and improved scalability, we propose a comprehensive migration plan towards an End-to-End Integrated-Optical-Packet-Network (E2-IOPN) for SP optical transport networks. We review various empirical challenges faced by a real SP during the transformation process towards E2-IOPN as well as the implementation of an as-built plan and a high-level design (HLD) for migrating towards lower cost-per-bit GPON, MPLS-TP, OTN and next-generation DWDM technologies. Then, we propose a longer-term strategy based on SDN and NFV approach that will offer rapid end-to-end service provisioning with costefficient centralized network control. We define CapEx and OpEx cost models and drive a cost comparative study that shows the benefit and financial impact of introducing new low-cost packet-based technologies to carry traffic from legacy and new services. To address the second issue, we first introduce an algorithm based on a stochastic geometry model (Voronoi Tessellation) to more precisely define MBH zones within a geographical area and more accurately calculate required traffic demands and related MBH infrastructure. In order to optimize the deployment and activation of the network resources in the MBH in an efficient and cost-effective way, we propose a novel method called BackHauling-as-a-Service (BHaaS) for network planning and Total Cost of Ownership (TCO) analysis based on required traffic demand and a "You-pay-only-for-what-you-use" approach. Furthermore, we enhance BHaaS performance by introducing a more service-aware method called Traffic-Profile-asa- Service (TPaaS) to further drive down the costs based on yearly activated traffic profiles. Results show that BHaaS and TPaaS may enhance by 22% the project benefit compared to traditional TCO model. Finally, we introduce a new cost (CapEx and OpEx) models for 5G multi-tenant Virtualized MBH (V-MBH) as part of our contribution to address the third issue. In fact, in order to enhance the resource sharing and maximize the network profits, we drive a novel pay-as-yougrow and optimization model for the V-MBH called Virtual-Backhaul-as-a-Service (VBaaS). VBaaS can serve as a planning tool to optimize the Project Profit Margin (PPM) while considering the TCO and the yearly generated Return-on-Investment (ROI). We formulate an MNO Pricing Game (MPG) for TCO optimization to calculate the optimal Pareto-Equilibrium pricing strategy for offered Tenant Service Instances (TSI). Then, we compare CapEx, OpEx, TCO, ROI and PPM for a specific use-case known in the industry as CORD project using Traditional MBH (T-MBH) versus Virtualized MBH (V-MBH) as well as using randomized versus Pareto-Equilibrium pricing strategies. The results of our framework offer SPs and MNOs a more precise estimation of traffic demand, an optimized infrastructure planning and yearly resource deployment as well as an optimized TCO analysis (CapEx and OpEx) with enhanced pricing strategy and generated ROI. Numerical results show more than three times increase in network profitability using our proposed solutions compared with Traditional MBH (T-MBH) methods

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

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

On greening optical access networks

With the remarkable growth of fiber-based services, the number of FTTx subscribers has been dramatically increasing in recent years. Owing to the environmental concern, reducing energy consumption of optical access networks has become an important issue for network designers. In Ethernet passive optical network (EPON), the optical line terminal (OLT) located at the central office broadcasts the downstream traffic to all optical network units (ONUs), each of which checks all arrival downstream packets to obtain those destined to itself. Since traffic of ONUs changes dynamically, properly defining the sleep mode for idle ONUs can potentially save a significant amount of energy. However, it is challenging to shut down an ONU receiver as the ONU needs to receive some downstream control packets to perform upstream transmission. In this framework, a novel sleep control scheme is proposed to address the downstream issue which can efficiently put ONU receivers to sleep. This dissertation further defines multiple levels of power saving in which the ONU disables certain functions based on the upstream and downstream traffic load. The proposed schemes are completely compatible with the multi-point control protocol (MPCP) and EPON standards. Elimination of the handshake process makes the sleep control schemes more efficient. Currently, OLTs also consume a significant amount of energy in EPON. Therefore, reducing energy consumption of OLT is as important as reducing energy consumption of ONUs; such requirement becomes even more urgent as OLT keeps increasing its provisioning data rate, and higher data rate provisioning usually implies higher energy consumption. Thus, a novel energy-efficient OLT structure, which guarantees services of end users with a smallest number of power-on OLT line cards, is proposed. More specifically, the number of power-on OLT line cards is adapted to the real-time incoming traffic. Also, to avoid service disruption resulted by powering off OLT line cards, a proper optical switch is equipped in OLT to dynamically configure the communications between OLT line cards and ONUs. By deploying a semi-Markov based technique, the performance characteristics of the sleep control scheme such as delay and energy-saving are theoretically analyzed. It is shown that, with proper settings of sleep control parameters, the proposed scheme can save a significant amount of energy in EPON

Networking technology adoption : system dynamics modeling of fiber-to-the-home

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology, Management, and Policy Program, 2005.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Page 244 blank.Includes bibliographical references (p. 241-243).A system dynamics model is developed and run to study the adoption of fiber-to-the-home as a residential broadband technology. Communities that currently do not have broadband in the United States are modeled. This case is of particular interest to U.S. policymakers, but also relevant to other regions concerned with economic development in rural areas. The model is used to explore the effects of government policy on fiber-to-the-home deployment and on the telecommunications supply chain. The research finds that government policy relating to broadband deployment has been based on a weak understanding of the dynamics involved, resulting in trial and error policy making that has unintended consequences. The thesis shows that the current monitoring of broadband deployment by the Federal Communications Commission is inadequate to contribute to the formation of reasoned policy decisions. The model is used to explore the consequences that different regulatory scenarios have on fiber-to-the-home deployment. Among the policy choices considered are: resale of fiber-to-the-home lines to competitive providers; low cost government loans for commercial deployments; rapid deployment to all communities currently without service; and a ban on municipal deployments. The current Rural Utilities Service loan program is also included in the model and its effects are analyzed. The model is used to examine the consequences for the optoelectronics industry of different deployment scenarios. It shows that the interests of consumers, regulators, and even service providers are in conflict with the interests of the optoelectronics industry which provides a critical component necessary for the service.(cont.) Strategies to help mitigate that conflict and to promote the health of the components industry are explored. Deployment of fiber-to-the-home is costly, and cost recovery is difficult for both incumbent and competitive service providers, especially in rural and suburban regions that do not currently have service. The interests of policy makers, service providers, and component suppliers need to be aligned to implement effective policy that encourages the deployment of broadband to unserved regions. The Federal Communications Commission needs to rearchitect its monitoring of service providers and their activities to better understand the status of deployment and how its policies can help or hinder.by Andjelka Kelic.Ph.D