WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks
Jingwen Tong, Wei Guo, Jiawei Shao, Qiong Wu, Zijian Li, Zehong Lin,, Jun Zhang

TL;DR
WirelessAgent introduces a novel LLM-based autonomous framework for wireless network management, demonstrating near-optimal throughput and significantly improved bandwidth utilization in complex scenarios.
Contribution
It presents WirelessAgent, the first framework integrating LLMs with cognitive modules for autonomous wireless network management.
Findings
44.4% higher bandwidth utilization than prompt-based methods
Only 4.3% below rule-based optimality in throughput
Achieves near-optimal network throughput across scenarios
Abstract
The rapid evolution of wireless networks presents unprecedented challenges in managing complex and dynamic systems. Existing methods are increasingly facing fundamental limitations in addressing these challenges. In this paper, we introduce WirelessAgent, a novel framework that harnesses large language models (LLMs) to create autonomous AI agents for diverse wireless network tasks. This framework integrates four core modules that mirror human cognitive processes: perception, memory, planning, and action. To implement it, we provide a basic usage based on agentic workflows and the LangGraph architecture. We demonstrate the effectiveness of WirelessAgent through a comprehensive case study on network slicing. The numerical results show that WirelessAgent achieves higher bandwidth utilization than the \emph{Prompt-based} method, while performing only below the…
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Taxonomy
TopicsIPv6, Mobility, Handover, Networks, Security
