Building a digital twin for intelligent optical networks [Invited Tutorial]
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 …
layer is crucial. Digital twin (DT) modeling, which relies on continuous learning with real-time …
A survey on QoT prediction using machine learning in optical networks
In optical networks, a connection (eg, light-path and light-tree) is set up to carry data from its
source to destination (s). When the optical signal transmits through the fiber links and optical …
source to destination (s). When the optical signal transmits through the fiber links and optical …
Feedforward and recurrent neural network-based transfer learning for nonlinear equalization in short-reach optical links
Neural network (NN)-based nonlinear equalizers have been shown effective for various
types of short-reach direct detection systems. However, they work best for a certain channel …
types of short-reach direct detection systems. However, they work best for a certain channel …
Lightpath QoT computation in optical networks assisted by transfer learning
Precise computation of the quality of transmission (QoT) of lightpaths (LPs) in transparent
optical networks has techno-economic importance for any network operator. The QoT metric …
optical networks has techno-economic importance for any network operator. The QoT metric …
An overview of ML-based applications for next generation optical networks
Over the past few decades, the demand for the capacity and reliability of optical networks
has continued to grow. In the meantime, optical networks with larger knowledge scales have …
has continued to grow. In the meantime, optical networks with larger knowledge scales have …
Comparison of domain adaptation and active learning techniques for quality of transmission estimation with small-sized training datasets
Machine learning (ML) is currently being investigated as an emerging technique to automate
quality of transmission (QoT) estimation during lightpath deployment procedures in optical …
quality of transmission (QoT) estimation during lightpath deployment procedures in optical …
Machine-learning-based telemetry for monitoring long-haul optical transmission impairments: methodologies and challenges
Current management of optical communication systems is conservative, manual-based, and
time-consuming. To improve this situation, building an intelligent closed-loop control system …
time-consuming. To improve this situation, building an intelligent closed-loop control system …
ML-assisted QoT estimation: a dataset collection and data visualization for dataset quality evaluation
Machine learning (ML)-assisted solutions for quality of transmission (QoT) estimation or
classification have received significant attention in recent years. However, due to the …
classification have received significant attention in recent years. However, due to the …
Performance studies of evolutionary transfer learning for end-to-end QoT estimation in multi-domain optical networks
This paper proposes an evolutionary transfer learning approach (Evol-TL) for scalable
quality-of-transmission (QoT) estimation in multi-domain elastic optical networks (MD …
quality-of-transmission (QoT) estimation in multi-domain elastic optical networks (MD …
Domain adaptation: The key enabler of neural network equalizers in coherent optical systems
We introduce the domain adaptation and randomization approach for calibrating neural
network-based equalizers for real transmissions, using synthetic data. The approach …
network-based equalizers for real transmissions, using synthetic data. The approach …