Machine learning (ML) has disrupted a wide range of science and engineering disciplines in
recent years. ML applications in optical communications and networking are also gaining …

The loss of hollow-core Nested Antiresonant Nodeless Fiber (NANF) has been steadily
decreasing lately, approaching that of standard Single-Mode Fiber (SMF). As for non-linear …

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 …

A Gaussian noise (GN) model, precisely accounting for an arbitrary frequency dependent
signal power profile along the link, is presented. This allows accurate evaluation of the …

To effectively operate multivendor disaggregated networks, the performance of the physical
layer needs to be assessed by a quality-of transmission estimator (QoT-E) delivering quick …

Dramatic increase in data traffic, future diffusion of bandwidth-hungry 5G connectivity and
evolving services like virtual reality and cloud will require the implementation of the open …

Equalization enhanced phase noise (EEPN) occurs due to the interplay between laser
phase noise and electronic dispersion compensation (EDC) module. It degrades …

Due to the capability of the physics-informed neural network (PINN) to solve complex partial
differential equations automatically, it has revolutionized the field of scientific computing …

Recently, end-to-end deep learning (E2EDL) has been proposed for communication
systems to improve the overall performance of systems. In the domain of optical fiber …

Machine learning (ML) is more and more used to address the challenges of managing the
physical layer of increasingly heterogeneous and complex optical networks. In this tutorial …