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.

翻译：未来无线通信网络正从以数据为中心、面向设备的连接模式，转向基于任务导向连接提供智能沉浸式体验的范式，尤其是在预训练基础模型(PFM)蓬勃发展与6G原生人工智能(AI)愿景演进的背景下。因此，在6G中重构设备与服务器之间的协作模式并构建原生智能库变得至关重要。本文从数据、智能和网络三个维度分析了实现6G原生AI面临的挑战，进而提出一种基于基础模型的6G原生AI框架，给出意图感知型PFM的定制化方法，构建面向任务的AI工具集，并阐述了新型云边端协同范式。作为实际应用案例，我们将该框架应用于无线通信系统的编排优化，实现了最大总速率并给出初步评估结果。最后，本文指出了实现6G原生AI的研究方向。