SOLID: System-wide Operation via Learning In-device Dissimilarities

Description of the granted funding

The key challenge in wireless communications is increasing the data rate at the device. To achieve that, modern wireless communication standards employ so-called MIMO (multiple-input multiple-output) techniques that allow parallelizing the data transmission over multiple streams using multiple device antennas. However, the growing diversity of the device types (not only handsets but also aerial vehicles, automobiles, robots, etc.) and high mobility challenge the current MIMO design for 5G and beyond networks. This project is a cooperation among wireless communications experts from North Carolina State University (NC State) and Tampere University (TAU). It develops machine learning (ML)-based solutions to empower devices to learn optimal antenna configurations collaboratively. The project team will design novel methods which enable the optimization of advanced MIMO beam solutions specifically tailored to the highly diverse and dynamic devices
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Starting year

2023

End year

2025

Granted funding

Sergey Andreev Orcid -palvelun logo
349 965 €

Funder

Research Council of Finland

Funding instrument

Kahdenväliset yhteistyösopimukset, yhteishaut

Other information

Funding decision number

357721

Fields of science

Electronic, automation and communications engineering, electronics

Research fields

Tietoliikennetekniikka

Identified topics

5G, 6G, wireless networks, wireless communication