Innovative Cross-Layer Optimization Techniques for the Design of Optical Networks

Abstract

AbstractOptical networks have become indispensable in the era of 5G-and-beyond communications, supporting applications that require unprecedented capacity, reliability, and high Quality-of-Transmission (QoT) of lightpaths. To meet these requirements, network operators strive to provide innovative solutions while managing network costs effectively. This work summarizes the main findings of my Ph.D. thesis Innovative Cross-Layer Optimization Techniques for the Design of Filterless and Wavelength-Switched Optical Networks, that has been conducted in partnership with an industrial partner, SM-Optics. The main objective of the Ph.D. thesis is to investigate solutions to reduce network costs while enabling network expandability through novel network architectures. To ensure cost savings and scalability, (1) we optimize the deployment of Optical Amplifiers (OA) while accurately modeling physical layer impairments in filterless networks, (2) we propose a modular node architecture relying on pluggable devices and a scalable add/drop section at the node level for traffic grooming and capacity increase, and (3) we investigate the application of Machine Learning (ML)—regression approaches to estimate lightpaths’ QoT as they allow to make informed decisions about how conservative or aggressive a network operator can be when taking network planning choices, i.e., deploying a new lightpath. Numerical evaluations show that our proposed approaches achieve significant cost savings compared to benchmark approaches: (1) $${\sim }50\%$$ ∼ 50 % savings in OA cost, (2) $${\sim }50\%$$ ∼ 50 % savings in node architecture equipment cost, (3) $${\sim }70\%$$ ∼ 70 % in penalty costs for deploying wrong lightpath configurations.