Toward 6G Native-AI Network: Foundation Model-Based Cloud-Edge-End Collaboration Framework
Xiang Chen, Zhiheng Guo, Xijun Wang, Chenyuan Feng, Howard H. Yang, Shuangfeng Han, Xiaoyun Wang, Tony Q. S. Quek
Abstract
Future wireless communication networks are in a position to move beyond data-centric, device-oriented connectivity and offer intelligent, immersive experiences based on multi-agent collaboration, 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 agents, and constructing native intelligence libraries become critically important in 6G. In this article, we analyze the challenges of achieving 6G native AI from the perspectives of data, AI models, and operational paradigms. Then, we propose a 6G native AI framework based on foundation models, provide an integration method for the expert knowledge, present the customization for two kinds of PFM, and outline a novel operational paradigm for the native AI framework. As a practical use case, we apply this framework for orchestration, achieving the maximum sum rate within a cell-free massive MIMO system, and presenting preliminary evaluation results. Finally, we outline research directions for achieving native AI in 6G.