WDM Network Issues: QOT Impairments, Dynamic Bandwidth Allocation, Modeling and Monitoring Schemes based on ML

Abstract

Machine Learning (ML) has recently drawn the interest of both academics and professionals in the optical networking area to solve several problems. This trend has been triggered largely by the vast amount of available data (i.e., signal strength metrics, network alerts, etc.) and the large range of optimization criteria available in today's optical networks (such as modulation format, light path routes, transport wavelength, etc.). WDM systems are popular in the telecommunications industry. It increases network bandwidth without laying more cable. WDM technology in optical communication transports multiple optical carrier signals on a single fibre using various wavelengths of laser light. Wavelength division multiplexing (WDM) is an efficient strategy for leveraging the wide bandwidth of optical fibres to meet the rapid rise in demand for bandwidth on the Internet. WDM plays an essential role in both core and access networks in the communication network and faces various issues like its network performance is affected due to QoT impairments, Dynamic Bandwidth Allocation problem, Modeling, and Monitoring issues. Physical impairments are an issue in WDM and can cause calls to be blocked if the transmission quality is calculated in terms of minimum bit-error-rate. Dynamic allocation of bandwidth in WDM provides a crucial challenge for the effective and equal usage of the passive optical network upstream bandwidth when supporting the QoS specifications of various traffic groups. Fast attack monitoring can prevent the failure of vast volumes of data on all-optical networks. So, monitoring and modeling in a WDM network is also a critical issue. To solve these issues in WDM networks, more intelligence resources in scalable optical networks are necessary. ML techniques are demonstrating superiority in solving these issues. In this paper, a small detail about the WDM network is presented. Then, some WDM issues like QOT impairments, Dynamic Bandwidth Allocation, and modeling and monitoring are discussed, and then, in the next section focus on some ML techniques for solving these issues are presented.