Generative AI for Next Generation Radio Access Networks via FD-RAN: Concepts, Methodologies, and Applications

The recent revolutionary development of AI-generated content (AIGC) services, exemplified by ChatGPT, marks a substantial stride forward in the field of generative AI (GAI). The cutting-edge GAI models are also envisioned to revolutionize the next-generation radio access networks for 6G. In this article, we specifically delve into the fully-decoupled RAN (FO-RAN), a novel architecture featuring extreme flexibility in terms of spectrum resource utilization and personalized service provision. We investigate how to enhance its capabilities with GAI, including feedback-free transmission, cooperative resource scheduling, and user-centric service provision. Furthermore, we conduct a case study on enhancing geolocation-based precoding in FD-RAN with variational autoencoders for channel augmentation. We also discuss future directions of GAI for RAN to support many more emerging applications and user demands.

Introduction

The recent explosion of AI-generated content (AIGC) applications, exemplified by ChatGPT, marks a significant leap in AI capabilities. These generative AI (GAI) models, diverging from traditional AI, are not just analytical tools but creators, synthesizing data in ways that mimic human-like understanding and creativity, leading to a variety of applications from chatbots to image, music, and video generation.