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 …

Failure management plays a role of capital importance in optical networks to avoid service
disruptions and to satisfy customers' service level agreements. Machine learning (ML) …

Photonic crystal fibers (PCFs) are the specialized optical waveguides that led to many
interesting applications ranging from nonlinear optical signal processing to high-power fiber …

This paper proposes a self-taught anomaly detection framework for optical networks. The
proposed framework makes use of a hybrid unsupervised and supervised machine learning …

In optical transport networks the quality of transmission (QoT) is estimated before
provisioning new connections or upgrading existing ones. Traditionally, a physical layer …

Recently, machine learning (ML) has attracted the attention of both researchers and
practitioners to address several issues in the optical networking field. This trend has been …

Chaotic optical communications were originally proposed to provide high-level physical
layer security for optical communications. Limited by the difficulty of chaos synchronization …

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 …

In space-division multiplexing elastic optical networks (SDM-EONs), it is important to handle
the complex resource optimization (RO) problem due to the coexistence of the requests that …

Over the last decade, Passive Optical Networks (PONs) have emerged as an ideal
candidate for next-generation broadband access networks. Meanwhile, machine learning …