Artificial intelligence (AI) is expected to play an important role in the sixth-generation (6G) air interface design, but making the air interface truly AI-native requires more than applying learning algorithms to individual radio functions. The deeper challenge is architectural: once AI influences how the user equipment and network interpret, predict, and adapt radio behavior, the air interface must provide common protocol semantics for coordinating such intelligence across vendors and deployments. This article presents a 3rd generation partnership project (3GPP) oriented perspective on the protocol framework for AI-native 6G air interface. We argue that standardization should preserve implementation freedom by avoiding prescription of model architectures, training methods, or model weights. Instead, 6G should define the protocol framework needed for interoperable AI operation, including how AI-enabled functions are configured, validated, activated, monitored, and safely reverted to conventional operation. Neural receiver assisted reference signal adaptation is used as a case study to concretely show this broader architectural shift.
The rapid development of agentic AI and multi-agent systems is establishing AI agent communication as a fundamental requirement for the future Internet. While a diverse array of agent communication protocols has recently emerged, these solutions currently suffer from interoperabi…
As artificial intelligence (AI) evolves from static perception to generative reasoning and autonomous agency, the fundamental principles of wireless communications are undergoing a paradigm shift. The classical Shannon paradigm, centered on reliable bit-level reconstruction for u…
Standards bodies, including TM Forum, 3GPP, and ETSI, are converging on Agentic AI as the foundation for next-generation network management, where Large AI Model (LAM)-based agents autonomously interpret intent, coordinate resources, and adapt operational behaviors at runtime. Ho…
Sixth-generation (6G) networks are moving toward AI-native operation, where learning modules are embedded across the radio access network (RAN), edge, and core. This transition requires learning from limited labels, heterogeneous wireless and network data, partial observations, n…
Integrated Sensing and Communication (ISAC) couples radio sensing, data transmission, and control actions within a single closed-loop system. When Artificial Intelligence (AI)-driven policies adapt sensing and communication online across a variety of sensing tasks and objectives,…
Beyond traditional connectivity, 6G is envisioned to transform mobile networks into a distributed fabric that provides native integrated communication, computing, and intelligence services. AI-native terminals (e.g., robots, autonomous vehicles, and smart glasses) require real-ti…