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

TitleGenerative AI for Next Generation Radio Access Networks via FD-RAN: Concepts, Methodologies, and Applications
Authors
Shi, YuhangChen, JiachengLiu, ZongxiXu, YuntingDu, HongyangZhou, HaiboNiyato, Dusit
Issue Date1-Jan-2025
PublisherInstitute of Electrical and Electronics Engineers
Citation
IEEE Communications Magazine, 2025, v. 63, n. 4, p. 80-86 How to Cite?
DOI: http://dx.doi.org/10.1109/MCOM.002.2400161
Abstract

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.


Persistent Identifierhttp://hdl.handle.net/10722/362102
ISSN
0163-6804
2023 Impact Factor: 8.3
2023 SCImago Journal Rankings: 5.631

 

DC FieldValueLanguage
dc.contributor.authorShi, Yuhang-
dc.contributor.authorChen, Jiacheng-
dc.contributor.authorLiu, Zongxi-
dc.contributor.authorXu, Yunting-
dc.contributor.authorDu, Hongyang-
dc.contributor.authorZhou, Haibo-
dc.contributor.authorNiyato, Dusit-
dc.date.accessioned2025-09-19T00:32:00Z-
dc.date.available2025-09-19T00:32:00Z-
dc.date.issued2025-01-01-
dc.identifier.citationIEEE Communications Magazine, 2025, v. 63, n. 4, p. 80-86-
dc.identifier.issn0163-6804-
dc.identifier.urihttp://hdl.handle.net/10722/362102-
dc.description.abstract<p>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.</p>-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.relation.ispartofIEEE Communications Magazine-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleGenerative AI for Next Generation Radio Access Networks via FD-RAN: Concepts, Methodologies, and Applications-
dc.typeArticle-
dc.identifier.doi10.1109/MCOM.002.2400161-
dc.identifier.scopuseid_2-s2.0-105003377332-
dc.identifier.volume63-
dc.identifier.issue4-
dc.identifier.spage80-
dc.identifier.epage86-
dc.identifier.eissn1558-1896-
dc.identifier.issnl0163-6804-

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