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.

2023

2026