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Artificial intelligence and machine learning methods for intelligent optical networks

Grant number: 23/01433-0
Support Opportunities:Scholarships abroad - Research
Effective date (Start): September 01, 2023
Effective date (End): August 31, 2024
Field of knowledge:Engineering - Electrical Engineering - Telecommunications
Principal Investigator:Gustavo Sousa Pavani
Grantee:Gustavo Sousa Pavani
Host Investigator: Christine Tremblay
Host Institution: Centro de Matemática, Computação e Cognição (CMCC). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil
Research place: Université du Québec à Montréal (UQÀM), Canada  
Associated research grant:15/24341-7 - New strategies to confront with the threat of capacity exhaustion, AP.TEM

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) techniques may play a significant role in the intelligent control of optical networks by offering improved performance over traditional methods. This is of particular importance as optical networks constitute the foundations of many of today's communication systems. In this project, we propose the investigation of the Quality of Transmission (QoT) and Optical Performance Monitoring (OPM) by using Supervised Learning models. Those models can be used to estimate and forecast the QoT of the lightpaths, or to provide early detection of anomalies in the network that may affect the QoT of the lightpaths. We also investigate, for the first time, the routing in Filterless Optical Networks (FON) equipped with programmable optical switches by means of the Ant Colony Optimization (ACO) algorithm. In effect, the ACO algorithm has been successfully employed in many different optical networking architectures, such as Wavelength-Switched and Elastic Optical Networks. In this context, it can be considered a suitable candidate for controlling the FON. Finally, this project establishes a new international collaboration with the research group led by Prof. Christine Tremblay at ÉTS-Montreal. (AU)

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