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.