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.
National Science Foundation (NSF)
The grant—running from October 1, 2022 to September 30, 2025—is for a total of $499,260.
Robert Wendell Heath Jr
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.