Project description
Harnessing machine learning will seamlessly optimise 5G-network management
Machine learning is a subset of AI in which algorithms and statistical models are used to analyse and draw inferences from patterns in data, enabling a system to learn, adapt and ‘make decisions’ without explicit instructions. As wireless communication networks become increasingly complex and must manage exponentially increasing amounts of data, network management and optimisation tools based on machine learning have the potential to significantly enhance efficiency and reliability. With the support of the Marie Skłodowska-Curie Actions programme, the WINDMILL project is establishing a training network to integrate wireless communications and machine learning for 5G networks and beyond.
Objective
With their evolution towards 5G and beyond, wireless communication networks are entering an era of massive connectivity, massive data, and extreme service demands. A promising approach to successfully handle such a magnitude of complexity and data volume is to develop new network management and optimization tools based on machine learning. This is a major shift in the way wireless networks are designed and operated, posing demands for a new type of expertise that requires the combination of engineering, mathematics and computer science disciplines. The ITN project WindMill addresses this need by providing Early Stage Researchers (ESRs) with an expertise integrating wireless communications and machine learning. The project will train 15 ESRs within a consortium of leading international research institutes and companies comprising experts in wireless communications and machine learning. This a very timely project, providing relevant inter-disciplinary training in an area where machine learning represents a meaningful extension of the current methodology used in wireless communication systems. Accordingly, the project will produce a new generation of experts, extremely competitive on the job market, considering the scale by which machine learning will impact the future and empower the individuals that are versed in it. The project will also nurture the sense of responsibility of the ESRs and the other participants through personal engagement in the training program and by promoting teamwork through collaborative joint projects.
Fields of science
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationstelecommunications networksmobile network5G
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationsradio technology
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Keywords
Programme(s)
Coordinator
9220 Aalborg
Denmark
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Participants (9)
06410 Biot
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08860 Castelldefels Barcelona
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02150 Espoo
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35122 Padova
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164 80 Stockholm
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91300 Massy
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08014 Barcelona
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
8092 Zuerich
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70839 Gerlingen-Schillerhoehe
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Partners (11)
Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
19801 Wilmington
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14850 Ithaca Ny
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85579 Neubiberg
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78701 2982 Austin
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08034 Barcelona
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70174 Stuttgart
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91192 Gif Sur Yvette
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
22203 Arlington
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22201 Arlington Va
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
69621 Villeurbanne Cedex
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
1331 Fornebu
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