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WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks

Jingwen Tong, Jiawei Shao, Qiong Wu, Wei Guo, Zijian Li, Zehong Lin, Jun Zhang

TL;DR

The paper tackles the rising complexity of wireless networks by introducing WirelessAgent, a generalizable AI-agent framework that uses large language models to perceive, remember, plan, and act in wireless environments. It integrates text understanding, multimodal data handling, retrieval-augmented reasoning, and tool-based action to autonomously manage tasks such as network slicing. A proof-of-concept case study on network slicing demonstrates that WirelessAgent can accurately infer user intent, allocate slice resources efficiently, and maintain performance under increasing load, outperforming traditional slice management. The work highlights the potential of LLM-based agents for scalable, autonomous control in 6G and beyond, while outlining directions for multimodal integration, explainability, security, and real-world deployment.

Abstract

Wireless networks are increasingly facing challenges due to their expanding scale and complexity. These challenges underscore the need for advanced AI-driven strategies, particularly in the upcoming 6G networks. In this article, we introduce WirelessAgent, a novel approach leveraging large language models (LLMs) to develop AI agents capable of managing complex tasks in wireless networks. It can effectively improve network performance through advanced reasoning, multimodal data processing, and autonomous decision making. Thereafter, we demonstrate the practical applicability and benefits of WirelessAgent for network slicing management. The experimental results show that WirelessAgent is capable of accurately understanding user intent, effectively allocating slice resources, and consistently maintaining optimal performance.

WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks

TL;DR

The paper tackles the rising complexity of wireless networks by introducing WirelessAgent, a generalizable AI-agent framework that uses large language models to perceive, remember, plan, and act in wireless environments. It integrates text understanding, multimodal data handling, retrieval-augmented reasoning, and tool-based action to autonomously manage tasks such as network slicing. A proof-of-concept case study on network slicing demonstrates that WirelessAgent can accurately infer user intent, allocate slice resources efficiently, and maintain performance under increasing load, outperforming traditional slice management. The work highlights the potential of LLM-based agents for scalable, autonomous control in 6G and beyond, while outlining directions for multimodal integration, explainability, security, and real-world deployment.

Abstract

Wireless networks are increasingly facing challenges due to their expanding scale and complexity. These challenges underscore the need for advanced AI-driven strategies, particularly in the upcoming 6G networks. In this article, we introduce WirelessAgent, a novel approach leveraging large language models (LLMs) to develop AI agents capable of managing complex tasks in wireless networks. It can effectively improve network performance through advanced reasoning, multimodal data processing, and autonomous decision making. Thereafter, we demonstrate the practical applicability and benefits of WirelessAgent for network slicing management. The experimental results show that WirelessAgent is capable of accurately understanding user intent, effectively allocating slice resources, and consistently maintaining optimal performance.
Paper Structure (21 sections, 4 figures)

This paper contains 21 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Agents for Wireless Networks
  3. From LLMs to AI Agents
  4. Principles of Agents for Wireless Networks
  5. The WirelessAgent Framework
  6. Perception
  7. Text understanding
  8. Multimodal processing
  9. Memory
  10. Planning
  11. Reasoning module
  12. Retrieval module
  13. Reflection module
  14. Action
  15. Text generation
  16. ...and 6 more sections

Figures (4)

  • Figure 1: The overview of the WirelessAgent framework. From left to right, the three parts are key supports, core modules, and basic usage.
  • Figure 2: An illustration of WirelessAgent-enabled slice management, with eMBB and URLLC slices. This framework consists of two parts. The first part is the external environment, including human interaction and network conditions. The second part is the WirelessAgent, where different modules are related to different functions in the network slicing management task. In addition, an example of User 53 is used to visualize the agent's workflow.
  • Figure 3: An example of WirelessAgent used in the network slicing management task.
  • Figure 4: The resource occupation rate of the traditional and WirelessAgent network slicing management approaches in a cellular network, where users arrive sequentially. The total number of RBs in the eMBB and URLLC slices are 30 and 90, respectively. Each rectangular block in the bar chart represents the RBs allocated to a user. For example, there are a total of eight users in the eMBB slice for the traditional method when the number of users is 30.