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.
Authors
- Yuhang Shi (ORCID: https://orcid.org/0000-0002-6226-0970)
- Haibo Zhou (ORCID: https://orcid.org/0000-0002-6549-8917)
- Hongyang Du (ORCID: https://orcid.org/0000-0002-8220-6525)
- Yunting Xu (ORCID: https://orcid.org/0000-0001-6341-4698)
- Zongxi Liu (ORCID: https://orcid.org/0009-0000-7650-9215)
- Dusit Niyato (ORCID: https://orcid.org/0000-0002-7442-7416)
- Jiacheng Chen (ORCID: https://orcid.org/0000-0002-2021-4570)
Institutions
- Nanyang Technological University (SG)
- Peng Cheng Laboratory (CN)
- Nanjing University (CN)
- University of Hong Kong (HK)
Publication Details
- Journal
- IEEE Communications Magazine
- Published
- 2025-03-31
- DOI
- https://doi.org/10.1109/mcom.002.2400161
- Primary Topic
- Advanced MIMO Systems Optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Research Foundation Singapore
- National Natural Science Foundation of China
- State Key Laboratory of Polymer Physics and Chemistry