We discuss various applications of machine learning techniques in different aspects of
optical communications and networking including optical performance monitoring, fiber …

In the optical networks, the dynamicity, the complexity and the heterogeneity have
dramatically increased owing to the deployment of advanced coherent techniques, and the …

Abstract Machine learning (ML) has been remarkably successful in transforming numerous
scientific and technological fields in recent years including computer vision, natural …

Optical network plays an important role in telecommunication networks, which supports high-
capacity and long-distance transmission of Internet traffic. However, as the scaling and …

The requirement for the huge bandwidth-demanding applications has urged researchers to
develop improved data pipes that could allow smooth data flow. Revolutionary growth has …

Optical fiber communication industry has gained unprecedented opportunities and achieved
rapid progress in recent years. However, with the increase of data transmission volume and …

It's feasible that today's telecommunications networks may benefit greatly from the use of
machine learning (ML) methods to contemporary optical communication systems. Since our …

As optical networks undergo rapid development, the trade-offs among higher network
service capability, and increasing operating expense (OPEX) about operations …

During the last decade, optical networks have become “smart networks”. Software-defined
networks, software-defined optical networks, and elastic optical networks are some …

With the availability of huge bandwidth backbone networks in terms of optical fiber links,
there is a surge in data at the user end. It has been boosted further by the deployment of 5G …