# Generative AI for Game Theory-based Mobile Networking

arXiv: https://arxiv.org/abs/2404.09699 · HTML: https://mlsanity.com/paper/2404.09699/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2024.

## Built on by (most cited first, 12 of 12)

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- [When Numbers Start Talking: Implicit Numerical Coordination Among LLM-Based Agents](https://mlsanity.com/paper/2601.03846/) (2026)
- [LinguaGame: A Linguistically Grounded Game-Theoretic Paradigm for Multi-Agent Dialogue Generation](https://mlsanity.com/paper/2601.04516/) (2026)
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- [GARL: Game-Theoretic Reinforcement Learning for Multi-Agent Strategic Prioritisation](https://mlsanity.com/paper/2606.05002/) (2026)
- [When Numbers Start Talking: Numerical Signalling and Strategic Behaviour Among LLMs](https://mlsanity.com/paper/2610.03033/) (2026)

## Abstract (arXiv)

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 to 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.