Generative AI for Game Theory-based Mobile Networking
arxiv(2024)
摘要
With the continuous advancement of network technology, various emerging
complex networking optimization problems opened up a wide range of applications
utilizating of game theory. However, since game theory is a mathematical
framework, game theory-based solutions often require the experience and
knowledge of human experts. Recently, the remarkable advantages exhibited by
generative artificial intelligence (GAI) have gained widespread attention. In
this article, we propose a novel GAI-enabled game theory solution that combines
the powerful reasoning and generation capabilities of GAI to the design and
optimization of mobile networking. Specifically, we first outline the game
theory and key technologies of GAI, and then explore the advantages of
combining GAI with game theory. Then, we briefly review the advantages and
limitations of existing research and demonstrate the potential application
values of GAI applied to game theory in mobile networking. Subsequently, we
develop a game theory framework enabled by large language models (LLMs) 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.
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