Non-Terrestrial Integrated Access and Backhaul for 6G LEO Satellite MVNO

The developed system will be tested and validated on the state-of-the-art federated learning and network slicing testbed at NC State University.

This project seeks to create AI-based federated slicing and orchestration for managing constellation networks and providing AI/real-time services. The proposed designs will optimize the resources and networking configurations of ground and space tiers, as well as multiple constellations, to create high-throughput, reliable end-to-end transmissions. These designs will be tested and validated on the federated learning and network slicing testbed at NC State.

Sponsor

Principle Investigators

Shih-Chun Lin

More Details

This project aims to develop AI-native federated slicing and orchestration that enables the synergy of federated AI and network slicing for mega-constellation management and AI/real-time services. A federated edge learning setting facilitates multi-tier, multi-domain AI executions by jointly optimizing resource and networking configurations of ground and space tiers and multiple constellations. Hence, the proposed designs will create high-throughput, reliable end-to-end transmissions for global connectivity.