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Article: Generative AI for Game Theory-Based Mobile Networking

TitleGenerative AI for Game Theory-Based Mobile Networking
Authors
Issue Date4-Feb-2025
PublisherInstitute of Electrical and Electronics Engineers
Citation
IEEE Wireless Communications, 2025, v. 32, n. 1, p. 122-130 How to Cite?
Abstract

With the continuous advancement of network technology, various emerging complex networking optimization problems have created a wide range of applications utilizing game theory. However, since game theory is a mathematical framework, game theory-based solutions often rely heavily on the experience and knowledge of human experts. Recently, the remarkable advantages exhibited by generative artificial intelligence (GAI) have gained widespread attention. In this work, we propose a novel GAI-enabled game theory solution that combines the powerful reasoning and generation capabilities of GAI with the design and optimization of mobile networking. Specifically, we first outline the game theory and key technologies of GAI and explore the advantages of combining GAI with game theory. Then, we review the contributions and limitations of existing research and demonstrate the potential application values of GAI applied to game theory in mobile networking. Subsequently, we develop a large language model (LLM)-enabled game theory framework to realize this combination and demonstrate the effectiveness of the proposed framework through a case study in secured UAV networks. Finally, we provide several directions for future extensions.


Persistent Identifierhttp://hdl.handle.net/10722/355284
ISSN
2023 Impact Factor: 10.9
2023 SCImago Journal Rankings: 5.926

 

DC FieldValueLanguage
dc.contributor.authorHe, Long-
dc.contributor.authorSun, Geng-
dc.contributor.authorNiyato, Dusit-
dc.contributor.authorDu, Hongyang-
dc.contributor.authorMei, Fang-
dc.contributor.authorKang, Jiawen-
dc.contributor.authorDebbah, Merouane-
dc.contributor.authorHan, Zhu-
dc.date.accessioned2025-04-01T00:35:25Z-
dc.date.available2025-04-01T00:35:25Z-
dc.date.issued2025-02-04-
dc.identifier.citationIEEE Wireless Communications, 2025, v. 32, n. 1, p. 122-130-
dc.identifier.issn1536-1284-
dc.identifier.urihttp://hdl.handle.net/10722/355284-
dc.description.abstract<p>With the continuous advancement of network technology, various emerging complex networking optimization problems have created a wide range of applications utilizing game theory. However, since game theory is a mathematical framework, game theory-based solutions often rely heavily on the experience and knowledge of human experts. Recently, the remarkable advantages exhibited by generative artificial intelligence (GAI) have gained widespread attention. In this work, we propose a novel GAI-enabled game theory solution that combines the powerful reasoning and generation capabilities of GAI with the design and optimization of mobile networking. Specifically, we first outline the game theory and key technologies of GAI and explore the advantages of combining GAI with game theory. Then, we review the contributions and limitations of existing research and demonstrate the potential application values of GAI applied to game theory in mobile networking. Subsequently, we develop a large language model (LLM)-enabled game theory framework to realize this combination and demonstrate the effectiveness of the proposed framework through a case study in secured UAV networks. Finally, we provide several directions for future extensions.</p>-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.relation.ispartofIEEE Wireless Communications-
dc.titleGenerative AI for Game Theory-Based Mobile Networking-
dc.typeArticle-
dc.identifier.doi10.1109/MWC.007.2400133-
dc.identifier.scopuseid_2-s2.0-85218345900-
dc.identifier.volume32-
dc.identifier.issue1-
dc.identifier.spage122-
dc.identifier.epage130-
dc.identifier.eissn1558-0687-
dc.identifier.issnl1536-1284-

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