# A Game-Theoretic Learning Framework for Multi-Agent Intelligent Wireless Networks

## TL;DR

This paper proposes a game-theoretic learning framework for multi-agent wireless networks that enhances coordination, decision-making, and anti-jamming capabilities in dynamic, uncertain environments, validated through real-world experiments.

## Contribution

It introduces a novel game-theoretic learning framework tailored for intelligent wireless networks, integrating AI and game theory for improved multi-agent coordination and security.

## Key findings

- Effective anti-jamming demonstrated in real-world testbed
- Enhanced multi-agent coordination in dynamic environments
- Game-theoretic methods outperform traditional approaches

## Abstract

In this article, we introduce a game-theoretic learning framework for the multi-agent wireless network. By combining learning in artificial intelligence (AI) with game theory, several promising properties emerge such as obtaining high payoff in the unknown and dynamic environment, coordinating the actions of agents and making the adversarial decisions with the existence of malicious users. Unfortunately, there is no free lunch. To begin with, we discuss the connections between learning in AI and game theory mainly in three levels, i.e., pattern recognition, prediction and decision making. Then, we discuss the challenges and requirements of the combination for the intelligent wireless network, such as constrained capabilities of agents, incomplete information obtained from the environment and the distributed, dynamically scalable and heterogeneous characteristics of wireless network. To cope with these, we propose a game-theoretic learning framework for the wireless network, including the internal coordination (resource optimization) and external adversarial decision-making (anti-jamming). Based on the framework, we introduce several attractive game-theoretic learning methods combining with the typical applications that we have proposed. What's more, we developed a real-life testbed for the multi-agent anti-jamming problem based on the game-theoretic learning framework. The experiment results verify the effectiveness of the proposed game-theoretic learning method.

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Source: https://tomesphere.com/paper/1812.01267