On the suitability, requisites, and challenges of machine learning [Invited]

Abstract

The introduction of 5G, the increasing number of connected devices, and the exponential growth of services relying on connectivity are pressuring multilayer networks to improve their scaling, efficiency, and controlling capabilities. However, enhancing those features consistently results in a significant amount of complexity in operating the resources available across heterogeneous vendors and technology domains. Thus, multilayer networks should become more intelligent in order to be efficiently managed, maintained, and optimized. In this context, we are witnessing an increasing interest in the adoption of artificial intelligence (AI) and machine learning (ML) in the design and operation of multilayer optical transport networks. This paper provides a brief introduction to key concepts in AI/ML, highlighting the conditions under which the use of ML is justified, on the requisites to deploy a data-driven system, and on the challenges faced when moving toward a production environment. As far as possible, some key concepts are illustrated using two realistic use-cases applied to multilayer optical networks: cognitive service provisioning and quality of transmission estimation.

Linear Regression vs. Deep Learning for Signal Quality Monitoring in Coherent Optical Systems

“…It can dynamically adjust network configurations e.g., modulation formats, data rate, spectrum planning, according to different channel conditions to increase the utilization efficiency of bandwidth resources [3]. However, these enhanced features will lead to the increased complexity of manipulating available resources for heterogeneous vendors and technical domains [4]. Consequently, it is necessary to implement a comprehensive and intelligent optical performance monitoring (OPM) system to ensure effective network management and diagnose faults [3][4][5][6].…”

“…However, these enhanced features will lead to the increased complexity of manipulating available resources for heterogeneous vendors and technical domains [4]. Consequently, it is necessary to implement a comprehensive and intelligent optical performance monitoring (OPM) system to ensure effective network management and diagnose faults [3][4][5][6]. There are generally two types of OPM that gained considerable attention in research: OPM at intermediate network nodes and OPM at end nodes [7][8][9][10].…”