Mikko Valkama and Sergey Andreev awarded funding by the Academy of Finland and the National Science Foundation
The aim of this joint call is to support research collaboration in the fields of artificial intelligence (AI) and/or wireless communication technologies. The National Science Foundation and the Academy of Finland collaborate via a Lead Agency Opportunity, in which the NSF acts as the Lead Agency. In this approach, proposers from both countries collaborated to write a single proposal.
The overall goal of the partnership between the Academy of Finland and the NSF is to enable and facilitate research collaboration between US and Finnish researchers. This collaboration expects to generate valuable discoveries and innovations that may lead to enhancements in multiple areas of science and technology.
The partnership achieves its goals through research projects in which the funding agencies fund the elements of research undertaken by researchers based in their respective nations.
In Finland, the partnership and the joint call is guided by the Research Council for Natural Sciences and Engineering at the Academy of Finland and managed under the Research, Development and Innovation Programme ICT 2023. Proposals were accepted for collaborative research in areas at the intersection of core research programmes in the NSF Directorate for Computer and Information Science and Engineering (CISE) and the Academy’s Finnish Research Flagships in which Finland has widespread demonstrated expertise, that is, artificial intelligence (AI) and wireless communication technologies.
NSF-AoF: CNS Core: Small: Machine Learning Based Physical Layer and Mobility Management Solutions Towards 6G
01.01.2023 - 31.12.2025
5G evolution and future 6G cellular networks are targeting operation at higher millimeter wave and sub-THz bands due to large available channel bandwidths. However, the use of these bands for mobile radio access imposes substantial technical challenges, including the quality, cost- and energy-efficiency of the electronics, the extreme path loss and propagation characteristics, and the overall deployment costs to provide indoor and outdoor network coverage with mobility support. Considering these challenges, this project will harness machine learning algorithms for designing physical layer technologies and network management procedures that aim to improve robustness and reliability of connectivity under mobility. The project’s expected contributions are at the forefront of emerging 6G standard and applications of modern machine learning tools in wireless communications at high frequency bands. The project is a joint effort between Tampere University, Finland, and UCLA, US.
SOLID: System-wide Operation via Learning In-device Dissimilarities
01.01.2023 - 31.12.2025
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.