Way to Build Native AI-driven 6G Air Interface: Principles, Roadmap, and Outlook

--Artificial intelligence (AI) is expected to serve as a foundational capability across the entire lifecycle of 6G networks, spanning design, deployment, and operation. This article proposes a native AI-driven air interface architecture built around two core characteristics: compression and adaptation. On one hand, compression enables the system to understand and extract essential semantic information from the source data, focusing on task relevance rather than symbol-level accuracy. On the other hand, adaptation allows the air interface to dynamically transmit semantic information across diverse tasks, data types, and channel conditions, ensuring scalability and robustness. This article first introduces the native AI-driven air interface architecture, then discusses representative enabling methodologies, followed by a case study on semantic communication in 6G non-terrestrial networks. Finally, it presents a forward-looking discussion on the future of native AI in 6G, outlining key challenges and research opportunities. The sixth generation (6G) of wireless networks is envisioned as a foundational transformation that extends far beyond incremental performance improvements. According to the International Telecommunication Union (ITU), 6G will support a new class of usage scenarios such as ubiquitous connectivity, integrated sensing and communication (ISAC), and artificial intelligence (AI) and communications [1]. This work was partly supported by the National Natural Science Foundation of China under Grants 62293480, 62293481, and 62471065. Co-corresponding authors: Kai Niu and Yiming Liu.) Ping Zhang, Yiming Liu, Nan Ma, Xiaodong Xu, Wenjun Xu, and Mengying Sun are with State Key Laboratory of Network and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.