Towards AI-Driven RANs for 6G and Beyond: Architectural Advancements and Future Horizons

TL;DR

This paper discusses the transition to AI-driven radio access networks (AI-RAN) for 6G, proposing a novel framework, reviewing architectural evolution, and exploring enabling technologies and future research directions.

Contribution

It introduces a new AI-RAN framework, evaluates its performance in intelligent resource management, and reviews key enabling technologies and deployment challenges for 6G networks.

Findings

Proposed a novel AI-RAN framework for 6G networks.

Evaluated AI-RAN performance in resource optimization scenarios.

Identified key enablers like digital twins, IRSs, and GenAI for AI-RAN development.

Abstract

It is envisioned that 6G networks will be supported by key architectural principles, including intelligence, decentralization, interoperability, and digitalization. With the advances in artificial intelligence (AI) and machine learning (ML), embedding intelligence into the foundation of wireless communication systems is recognized as essential for 6G and beyond. Existing radio access network (RAN) architectures struggle to meet the ever growing demands for flexibility, automation, and adaptability required to build self-evolving and autonomous wireless networks. In this context, this paper explores the transition towards AI-driven RAN (AI-RAN) by developing a novel AI-RAN framework whose performance is evaluated through a practical scenario focused on intelligent orchestration and resource optimization. Besides, the paper reviews the evolution of RAN architectures and sheds light on key enablers of AI-RAN including digital twins (DTs), intelligent reflecting surfaces (IRSs), large generative AI (GenAI) models, and blockchain (BC). Furthermore, it discusses the deployment challenges of AI-RAN, including technical and regulatory perspectives, and outlines future research directions incorporating technologies such as integrated sensing and communication (ISAC) and agentic AI.