AutoMAS: A Generic Multi-Agent System for Algorithm Self-Adaptation in Wireless Networks

TL;DR

AutoMAS introduces a multi-agent system that enables wireless networks to autonomously select optimal algorithms in dynamic environments, enhancing adaptability and accuracy in tasks like channel estimation.

Contribution

This work presents AutoMAS, a novel AI-driven multi-agent system that dynamically adapts algorithm selection for wireless networks, improving performance in changing conditions.

Findings

AutoMAS achieves the highest accuracy in diverse, changing environments.

Simulation confirms AutoMAS's effectiveness in 6G wireless tasks.

AutoMAS generalizes to various tasks beyond channel estimation.

Abstract

The wireless communication environment has the characteristic of strong dynamics. Conventional wireless networks operate based on the static rules with predefined algorithms, lacking the self-adaptation ability. The rapid development of artificial intelligence (AI) provides a possibility for wireless networks to become more intelligent and fully automated. As such, we plan to integrate the cognitive capability and high intelligence of the emerging AI agents into wireless networks. In this work, we propose AutoMAS, a generic multi-agent system which can autonomously select the most suitable wireless optimization algorithm according to the dynamic wireless environment. Our AutoMAS combines theoretically guaranteed wireless algorithms with agents' perception ability, thereby providing sounder solutions to complex tasks no matter how the environment changes. As an example, we conduct a case study on the classical channel estimation problem, where the mobile user moves in diverse environments with different channel propagation characteristics. Simulation results demonstrate that our AutoMAS can guarantee the highest accuracy in changing scenarios. Similarly, our AutoMAS can be generalized to autonomously handle various tasks in 6G wireless networks with high accuracy.