Enabling Resilient Access Equality for 6G LEO Satellite Swarm Networks
Shih‐Chun Lin, Chia-Hung Lin, Liang Chu, Shao‐Yu Lien
Abstract
Low earth orbit (LEO) mega-constellations, integrating government space systems and commercial practices, become enabling technologies for the sixth generation (6G) networks due to their excellent merits of global coverage and ubiquitous services for military and civilian use cases. However, convergent LEO-based satellite networking infrastructures lack leveraging the synergy of space and terrestrial systems. This paper extends conventional cloud platforms with serverless edge learning architectures for 6G satellite swarm ecosystems and provides a new distributed training design from a networking perspective. The proposed method dynamically orchestrates communications, computation functionalities, and resources among heterogeneous physical units to efficiently fulfill multi-agent deep reinforcement learning for service-level agreements. Innovative ecosystem enhancements, including ultra-broadband access, anti-jamming transmissions, resilient networking, and related open challenges, are investigated for end-to-end connectivity, communications, and learning performance.