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

TL;DR

This paper introduces a native AI-driven 6G air interface architecture emphasizing semantic understanding and adaptability, aiming to enhance scalability, robustness, and efficiency in future wireless networks.

Contribution

It proposes a novel AI-driven air interface design for 6G, focusing on semantic compression and adaptive transmission, with a case study on non-terrestrial networks.

Findings

Semantic compression improves data relevance and efficiency.

Adaptive transmission enhances robustness across diverse conditions.

Case study demonstrates potential benefits in 6G non-terrestrial networks.

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.