Generative AI for Game Theory-based Mobile Networking

TL;DR

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 and develops a large language model (LLM)-enabled game theory framework to realize this combination.

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 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.

Figures (4)

• Figure 1: The non-cooperative game framework contains multiple players and an environment in which players interact. Each player has a strategy space that represents the available actions and a utility function that evaluates the player's payoff from taking a certain action. Additionally, the objective of each player is to take actions that maximize their own payoff. The environment represents the medium through which players interact, that matches an action profile to a payoff profile. The flow of player interaction is as follows: in the first interaction (Step 1), each player takes an action from their strategy space and receives the corresponding payoff. In subsequent interactions (Step i), each player updates their action based on the observed strategies of other players to improve their own payoff. When the actions of all players no longer change, a Nash equilibrium is obtained.
• Figure 2: Part A shows the LLM-enabled game theory framework. The framework is based on a layered architecture consisting of an input layer, an augmented layer, a decision layer, and an output layer. The input layer captures user input queries. The augmented layer employs RAG technology to enhance user queries. The decision layer utilizes a pluggable LLM module to generate responses. The output layer returns the generated results to the user. Part B shows the flowchart of the proposed framework.
• Figure 3: The operation flow of RAG models and LLMs. In Part A, documents are loaded from the knowledge base, segmented into chunks, encoded into vectors, and stored in the vector database. Then, RAG retrieves the $K$ most relevant chunks to the query of user based on semantic relevance from the vector database. In Part B, the original query and the retrieved chunks are inputted together into LLMs to generate the final response.
• Figure 4: The experiment results of the UAV secure communication optimization case. The scenario module presents a system model of the considered scenario. The interaction module showcases the functionality of the proposed framework. The RAG augmentation module emphasizes the operational mechanism of RAG. The evaluation results module demonstrate the effectiveness of the proposed framework.