Autonomic optical transmission and networking requires machine learning (ML) models to
be trained with large datasets. However, the availability of enough real data to produce …

Optical network failure management (ONFM) is a promising application of machine learning
(ML) to optical networking. Typical ML-based ONFM approaches exploit historical monitored …

This paper presents a hybrid learning approach for cooperative fault management in multi-
domain optical networks (MD-ONs). The proposed approach relies on a broker-based MD …

To support the pervasive digital evolution, optical network infrastructures must be able to
quickly and effectively adapt to changes arising from traffic dynamicity or external factors …

This paper proposes an evolutionary transfer learning approach (Evol-TL) for scalable
quality-of-transmission (QoT) estimation in multi-domain elastic optical networks (MD …

As the deployment of fiber‐based broadband networks continues to accelerate, the number
of network facilities too is increasing exponentially. The network of optical fiber cables keeps …

With a focus on service interruptions occurring in optical networks, we propose a failure
prognostics scheme based on a bi-directional gated recurrent unit (BiGRU) from the …

The H2020 METRO-HAUL European project has architected a latency-aware, cost-effective,
agile, and programmable optical metro network. This includes the design of …

With the advance of elastic optical networks, optical communication systems are becoming
more flexible and dynamic. In this scenario, soft failures are more likely to occur due to …

This paper proposes a real-time and self-taught anomaly detection scheme for optical
networks using hybrid unsupervised/supervised learning. Evaluations with an experimental …