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

This collaborative project between NC State and Tampere University (TAU) aims to create a distributed learning framework for efficient beam management in 5G+/pre-6G MIMO communication systems. It combines wireless communications technologies with advanced distributed machine learning methods. The proposed approach uses side-information to learn how to map beam combinations to sensor inputs based on past results, leading to a recommendation engine that suggests the most effective TX-RX beam pairs. If successful, this research will benefit 5G and 6G wireless communication systems.

Sponsor

Principle Investigators

Robert Wendell Heath Jr

More Details

This collaboration between North Carolina State University (NC State) and Tampere University (TAU) aims to design a novel distributed learning framework for efficient beam management in 5G+/pre-6G MIMO communication systems. The proposed research agenda lies at the intersection of wireless communications technologies and advanced methods of distributed machine learning. The idea of the side-information approaches is to learn mappings between beam combinations and sensor inputs based on past performance, to create a recommendation engine that suggests a small number of TX-RX beam pairs out of a large possible combinations. This research, if successful, has application to 5G and 6G wireless communication systems.