Today's telecommunication networks have become sources of enormous amounts of widely
heterogeneous data. This information can be retrieved from network traffic traces, network …

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 …

Modulation classification is one of the key tasks for communications systems monitoring,
management, and control for addressing technical issues, including spectrum awareness …

An intelligent constellation diagram analyzer is proposed to implement both modulation
format recognition (MFR) and optical signal-to-noise rate (OSNR) estimation by using …

The trade-off between more user bandwidth and quality of service requirements introduces
unprecedented challenges to the next generation smart optical networks. In this regard, the …

The rapid development of information and wireless communication technologies together
with the large increase in the number of end-users have made the radio spectrum more …

We experimentally demonstrate the use of deep neural networks (DNNs) in combination
with signals' amplitude histograms (AHs) for simultaneous optical signal-to-noise ratio …

Self-tuning optical systems are of growing importance in technological applications such as
mode-locked fiber lasers. Such self-tuning paradigms require intelligent algorithms capable …

With the rapid development of optical communication systems, more advanced techniques
conventionally used in long-haul transmissions have gradually entered systems covering …

Abstract Machine learning (ML) and neuromorphic computing have been enforcing problem-
solving in many applications. Such approaches found fertile ground in optical …