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

2025-08-21

AI Summary

To address the challenge of dynamically adapting to diverse tasks, data modalities, and time-varying channel conditions across the full lifecycle of 6G networks, this paper proposes a native-AI-driven air-interface architecture centered on *semantic compression* and *environmental adaptation*, enabling a paradigm shift from bit-level to task-oriented semantic communication. The architecture tightly integrates deep learning with semantic communication theory to realize an end-to-end trainable air-interface processing framework, validated system-wide in non-terrestrial network (NTN) scenarios. Experimental results demonstrate that, compared to conventional approaches, the proposed method achieves a 42% improvement in semantic transmission efficiency and a 31% increase in task completion accuracy under time-varying channels and resource constraints, while maintaining strong scalability. This work establishes the first complete, deployable native-AI air-interface architecture paradigm for 6G intelligent networks.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Efficient ML / Green AI
• Natural Language Processing: Generation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Designing a native AI-driven air interface for 6G networks
• Enabling compression to extract essential semantic information
• Achieving adaptation across diverse tasks and channel conditions

Innovation

• Native AI-driven air interface architecture
• Compression for essential semantic information extraction
• Adaptation for dynamic transmission across conditions