To support the development of intelligent optical networks, accurate modeling of the physical
layer is crucial. Digital twin (DT) modeling, which relies on continuous learning with real-time …

Optical communication is developing rapidly in the directions of hardware resource
diversification, transmission system flexibility, and network function virtualization. Its …

The development of digital twins to represent the optical transport network might enable
multiple applications for network operation, including automation and fault management. In …

Optical networks generate a vast amount of diagnostic, control, and performance monitoring
data. When information is extracted from these data, reconfigurable network elements and …

It is estimated that 5G and the Internet of Things (IoT) will impact traffic, both in volume and
dynamicity, at unprecedented rates. Thus, to cost-efficiently accommodate these challenging …

We have proposed a dynamically configurable and fast optical impairment model for the
abstraction of the optical physical layer, enabling new capabilities such as indirect …

Techniques from artificial intelligence have been widely applied in optical communication
and networks, evolving from early machine learning (ML) to the recent deep learning (DL) …

Optical network automation requires accurate physical layer models, not only for
provisioning but also for real-time analysis. In particular, in-phase (I) and quadrature (Q) …

Digital twin networks (DTNs) are real-time replicas of physical networks. They are emerging
as a powerful technology for design, diagnosis, simulation, what-if-analysis, and artificial …

An artificial neural network (ANN) based transfer learning model is built for quality of
transmission (QoT) prediction in optical systems feasible with different modulation formats …