MENTOR — Machine LEarning in Optical NeTwORks

Results in brief

Machine LEarning in Optical NeTwORks

To date, optical fibre technologies have been a major driver of our societal progress as they enable global telecommunication and all high-capacity transmission systems. Optical networks have changed rapidly over the last decade, moving from fixed grid with direct-detection using C-band-only systems to current elastic optical networks with coherent technology and software defined networking. Now, C-band systems started being upgraded to C+L. This evolution is a consequence of an incoming exhaustion of the C-band due to the increase of bandwidth demand. The exploitation of bands beyond C is crucial and a promising technique to increase the throughput of optical networks. The EID MENTOR has addressed the most relevant challenges of modern optical communication by combining ML&AI techniques and multiband transmission. Its objective has been to provide world class training for six early-stage researchers to become leaders in this sector. Thanks to MENTOR’s consortium, we can access real datasets for network management and data monitoring, components, propagation impairments, and data traffic, essential for the training phase of algorithms. Alongside Coordinator the Aston Institute of Photonic Technologies, Aston University, the consortium consists of the Polytechnic University of Catalonia (UPC), the Technical University of Denmark (DTU), Sant'Anna School of Advanced Studies (SSSA), Telecom Italia Mobile (TIM), Fibercop, Infinera Germany, Infinera Portugal and Orange.

Data: CORDIS, © European Union

Project objective

Optical fiber networks is one of the major drivers of our societal progress and a key enabler of the global telecommunication infrastructure. Optical networks underwent considerable changes over the past decade, as consequence of a continuous growth (exceeding 20% per year) of bandwidth demand. The current growth sets strong requirements in terms of capacity and costs for the operators, which seek to decrease the cost per transmitted bit. Several solutions have been proposed, and among them wide-band is more favourable to network operators, compared to more/or novel fibers. However, wide-band optical system presents new major challenges: optical components must guarantee similar performance over a broad spectrum, network optimization is carried out on a non-flat spectrum and with a much larger number of channels making design, optimization and control a complex problem. Therefore, application of machine learning (ML) techniques is of the growing importance for high-capacity multi-band (MB) optical systems. ML is becoming the technique of choice to solve complex nonlinear technical problems, such as, advance component design and management of wide-band networks. The European Industrial Doctorate MENTOR presents a timely proposal to train 6 ESRs in the interdisciplinary field of high industrial importance: ML applications in multi-band optical communications. As ML can properly works only when a large amount of real data is available, it is crucial to bring together academic partners and the industry that provide access to the data. MENTOR consortium offers the strong industrial commitment of four large companies (CORIANT Germany and Portugal, ORANGE Labs and TELECOM ITALIA MOBILE) that significantly contributed in defining the research and training topics to be studied together with the world-leading academic partners in MENTOR. MENTOR will contribute to the European economy by design of the next generation of high-capacity optical networks.

Original text from CORDIS.

Participants

ASTON UNIVERSITY · BirminghamCoordinatorUnited Kingdom
CORIANT R&D GMBH · StuttgartGermany
DANMARKS TEKNISKE UNIVERSITET · Kongens LyngbyDenmark
INFINERA UNIPESSOAL LDA · CARNAXIDEPortugal
SCUOLA SUPERIORE DI STUDI UNIVERSITARI E DI PERFEZIONAMENTO S ANNA · PisaItaly
UNIVERSITAT POLITECNICA DE CATALUNYA · BARCELONASpain

MENTOR · Machine LEarning in Optical NeTwORks

Results in brief

Machine LEarning in Optical NeTwORks

Project objective

Participants

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