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

Ping Zhang, Kai Niu, Yiming Liu, Zijian Liang, Nan Ma, Xiaodong Xu, Wenjun Xu, Mengying Sun, Yinqiu Liu, Xiaoyun Wang, Ruichen Zhang

Abstract: 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.

External IDs:dblp:journals/tnse/ZhangNLLMXXSLWZ26