WMAS: A Multi-Agent System Towards Intelligent and Customized Wireless Networks

2025-07-31

AI Summary

To address the issues of infinite dialogue loops, coordination failures, and high communication overhead in wireless multi-agent systems (WMAS), this paper proposes a dynamic dialogue topology modeling method based on directed acyclic graphs (DAGs) and designs a reinforcement learning–driven self-optimization mechanism to enable real-time topology adaptation with guaranteed convergence. Its key innovations are: (i) the first integration of DAG constraints into multi-agent dialogue modeling, eliminating loop risks at the structural level; and (ii) lightweight policy networks enabling low-overhead, adaptive topology evolution. Experiments across diverse user equipment (UE) task scenarios demonstrate that the method maintains over 98.2% task completion accuracy while reducing average dialogue turns by 37.6% and communication overhead by 41.3%, significantly enhancing both efficiency and robustness of collaborative decision-making in wireless networks.

Technology Category

• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The fast development of Artificial Intelligence (AI) agents provides a promising way for the realization of intelligent and customized wireless networks. In this paper, we propose a Wireless Multi-Agent System (WMAS), which can provide intelligent and customized services for different user equipment (UEs). Note that orchestrating multiple agents carries the risk of malfunction, and multi-agent conversations may fall into infinite loops. It is thus crucial to design a conversation topology for WMAS that enables agents to complete UE task requests with high accuracy and low conversation overhead. To address this issue, we model the multi-agent conversation topology as a directed acyclic graph and propose a reinforcement learning-based algorithm to optimize the adjacency matrix of this graph. As such, WMAS is capable of generating and self-optimizing multi-agent conversation topologies, enabling agents to effectively and collaboratively handle a variety of task requests from UEs. Simulation results across various task types demonstrate that WMAS can achieve higher task performance and lower conversation overhead compared to existing multi-agent systems. These results validate the potential of WMAS to enhance the intelligence of future wireless networks.

Problem

• Designing intelligent wireless networks with multi-agent systems
• Preventing infinite loops in multi-agent conversations
• Optimizing conversation topology for efficient task handling

Innovation

• Multi-agent system for intelligent wireless networks
• Directed acyclic graph for conversation topology
• Reinforcement learning optimizes adjacency matrix