The example codes and numerical traces supplemented for article "Data augmentation for the supervised learning of nonlinearity compensation algorithms in optical communications"

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Creators: Neskorniuk, Vladislav ORCID logoORCID: https://orcid.org/0000-0002-3358-153X, Freire, Pedro J. ORCID logoORCID: https://orcid.org/0000-0003-3145-1018, Napoli, Antonio ORCID logoORCID: https://orcid.org/0000-0002-9264-9274, Spinnler, Bernhard, Schairer, Wolfgang, Prilepsky, Jaroslaw E. ORCID logoORCID: https://orcid.org/0000-0002-3035-4112, Costa, Nelson ORCID logoORCID: https://orcid.org/0000-0002-8678-5691 and Turitsyn, Sergei K. ORCID logoORCID: https://orcid.org/0000-0003-0101-3834
Keywords: data augmentation, supervised learning, machine learning, nonlinearity mitigation, digital signal processing, fiber-optic communications, dataset, code
Divisions: College of Engineering and Physical Sciences > Aston Institute of Photonics Technology (AIPT)
Funders: VN and PJF acknowledge support by the European Union’s Horizon 2020 research and innovation program under MSCA GA 766115 and 813144, respectively., SKT acknowledges support by the EPSRC project TRANSNET.
Projects: H2020 MSCA Fibre Optic nonlinear Technologies [FONTE] – a European Industrial Doctorate [EID] (Grant agreement 766115), H2020 MSCA Real-time Monitoring and Mitigation of Nonlinear Effects in Optical Networks [REAL-NET] – a European Industrial Doctorate [EID] (Grant agreement 813144), EPSRC Transforming networks - building an intelligent optical infrastructure [TRANSNET] (EP/R035342/1)
Data Publisher: Aston University
Date Made Available: 25 September 2020
Collection period:
FromTo
1 March 20203 October 2020
Identification Number: https://doi.org/10.17036/researchdata.aston.ac.uk.00000481

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Supplementary materials.zip
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BER_calc.py
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Data_augmentation_for_DNN.py
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Data_augmentation_for_PPD.py
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