Toward AI-Native 6G Air Interface: A 3GPP Perspective on Protocol Framework

Abstract

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.