🤖 AI Summary
This work addresses the lack of a unified protocol architecture for native AI support in 6G air interfaces, which hinders cross-vendor intelligent collaboration. From a 3GPP standardization perspective, it presents the first systematic protocol semantic framework that is agnostic to specific AI implementations, focusing on configuration, verification, activation, monitoring, and secure fallback mechanisms for AI functionalities while balancing interoperability and implementation flexibility. By integrating AI-enabled techniques—such as neural receiver-assisted adaptive reference signal design—and validating through representative use cases, the study demonstrates that the proposed architecture provides a scalable, secure, and backward-compatible protocol foundation for 6G networks.
📝 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.