Foundation Model Based Native AI Framework in 6G with Cloud-Edge-End
  Collaboration
Xiang Chen Zhiheng Guo Xijun Wang Howard H. Yang Chenyuan Feng Junshen Su Sihui Zheng Tony Q. S. Quek
Future wireless communication networks are in a position to move beyond
data-centric, device-oriented connectivity and offer intelligent, immersive
experiences based on task-oriented connections, especially in the context of
the thriving development of pre-trained foundation models (PFM) and the
evolving vision of 6G native artificial intelligence (AI). Therefore,
redefining modes of collaboration between devices and servers and constructing
native intelligence libraries become critically important in 6G. In this paper,
we analyze the challenges of achieving 6G native AI from the perspectives of
data, intelligence, and networks. Then, we propose a 6G native AI framework
based on foundation models, provide a customization approach for intent-aware
PFM, present a construction of a task-oriented AI toolkit, and outline a novel
cloud-edge-end collaboration paradigm. As a practical use case, we apply this
framework for orchestration, achieving the maximum sum rate within a wireless
communication system, and presenting preliminary evaluation results. Finally,
we outline research directions for achieving native AI in 6G.