LEArning-Driven and Evolved Radio for 6G Communication Systems

ongoing

6G-LEADER aims at evolving the PHY and RAN aspects of 6G communication networks by relying on the following pillars: 1) ML-empowered PHY algorithms with predictive capabilities towards fully autonomous operation; 2) full-duplex transceivers employing novel sparse antenna arrays and advanced digital self-interference cancellation; 3) non-orthogonal and random multiple access schemes to accommodate mass connectivity demands of users and machines; 4) goal-oriented semantic communications; and 5) an open and disaggregated RAN implementation with xApps integrating the advancements, achieved during the project’s lifetime.