Work Package 1
Work Package 2
Work Package 1 ESRs 1-5, Complexity reduction and data augmentation for ML&AI-based optical systems, led by Dr. Yaroslav Prylepskiy and Prof. Sergei Turitsyn of Aston University. This work package focused on data augmentation and neural networks (NNs) simplification for the improvement of ML&AI models utilized in optical transmission systems. Industry partners provided a large amount of real data that was used to validate the investigated techniques of data augmentation. The large power required by ML algorithms was the main blocking point for the implementation of these techniques within commercial products. We investigated and provided solutions to reduce the complexity of ML techniques.
Prof. Sergei Turitsyn, Aston University, UK
Project Co-ordinator, WP1 Leader
Dr. Yaroslav Prylepskiy, Aston University, UK
WP1 Leader
Work Package 2 ESRs 1-4 ML&AI for component characterisation and optimisation. This work package focused on the application of ML&AI techniques for design and optimization of optical components including lasers sources, frequency combs and optical amplifiers. ESRs explored and developed new ML&AI algorithms for noise characterization of laser sources and inverse design of optical amplifiers and laser sources. MB components (wideband amplifier, lasers, etc.) need to provide similar characteristics over a large spectrum. This quite complex task showed that it could be carried out more effectively by using ML&AI.
Prof. Darko Zibar, DTU (Technical University of Denmark)
Project Co-ordinator, WP1 Leader
Work Package 3
Work Package 4
Work Package 3 ESRs 2-5 AI-based control and management for ultra-wideband optical networks, led by Assoc. Prof Nicola Sambo of SSSA, Italy. This work package focused on the design and the implementation of the control and management plane for MB optical networks. MB transmission led to different performance depending on the selected transmission band. Consequently, complex routing, modulation format and spectrum assignment algorithms were required. New strategies were proposed to extract the highest benefit from the available transmission bands by employing ML&AI algorithms, such as genetic algorithms.
Associate Prof. Nicola Sambo, Scuola Superiore Sant’Anna, Pisa, Italy
Work Package 3 Leader
Work Package 4 ESRs 3-6 Distributed intelligence for network surveillance, led by Prof. Luis Velasco of UPC. Work package 4 focused on the development of techniques for sharing data/knowledge among cooperating entities (including disaggregated scenarios) while in operation. Proposed was a learning life-cycle20 to facilitate ML&AI deployment in real operator networks. WP4 closely collaborated with WP3 for the distributed intelligence to fit into the control & management architecture, and with WP1 exploited data augmentation techniques.
Prof. Luis Velasco, UPC Barcelona, Spain
Work Package 4 Leader
Work Package 5
Work Package 6
Work Package 5 ESRs 1-6 Recruitment, management and implementation. Led by MENTOR Project Manager Ms. Karola Woods.
Work Package 6 ESRs 1-6 Impact, dissemination and outreach. Led by Project Manager Ms. Karola Woods of Aston University, with partners.
We contributed to Europe’s economy by designing high-capacity optical networks
