大模型与智能体驱动的6G网络智能化研究现状、应用与展望*

doi:10.12267/j.issn.2096-5931.2026.10.009

信息通信技术与政策 ›› 2026, Vol. 52 ›› Issue (10): 72-79.doi: 10.12267/j.issn.2096-5931.2026.10.009

大模型与智能体驱动的6G网络智能化研究现状、应用与展望^*

Current status, applications, and prospects of 6G wireless network intelligence driven by large models and agents

李启喆¹^,², 孙茜颐³, 刘晨吉⁴, 杨武⁴, 朱光旭¹^,²

¹ 深圳市大数据研究院, 深圳 518172
² 香港中文大学(深圳)理工学院, 深圳 518172
³ 北京工业大学, 北京 100124
⁴ 中国移动通信集团江西有限公司, 南昌 330036

收稿日期:2026-09-10 出版日期:2026-10-25 发布日期:2026-10-28
通讯作者: 朱光旭
作者简介:
李启喆,深圳市大数据研究院与香港中文大学(深圳)理工学院联合培养博士生在读,主要研究方向为无线网络智能化、深度学习、人工智能赋能的无线网络优化等;
孙茜颐,北京工业大学人工智能专业本科生在读,主要研究方向为人工智能技术与应用、面向6G的通信与AI融合技术;
刘晨吉,中国移动通信集团江西有限公司高级工程师,主要从事无线网络智能体应用、低空网络规划与优化研究;
杨武,中国移动通信集团江西有限公司无线中心总经理,主要从事江西无线自智网络的构建工作
基金资助:
^*广东省基础与应用基础研究重大项目(2023B0303000001);国家自然科学基金项目(U25A20394);国家自然科学基金项目(62522118);国家自然科学基金项目(62271081)

LI Qizhe¹^,², SUN Qianyi³, LIU Chenji⁴, YANG Wu⁴, ZHU Guangxu¹^,²

¹ Shenzhen Research Institute of Big Data, Shenzhen 518172, China
² School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
³ Beijing Institute of Technology, Beijing 100124, China
⁴ China Mobile Communications Group Jiangxi Co., Ltd., Nanchang 330036, China

Received:2026-09-10 Online:2026-10-25 Published:2026-10-28
Contact: ZHU Guangxu

摘要/Abstract

摘要：

面向6G无线网络日益复杂的业务需求与运行环境,传统任务专用模型在跨任务适配和领域知识融合方面存在局限。大模型凭借知识表示与任务泛化能力,为无线网络智能化提供了新路径。梳理无线网络大模型构建、无线任务应用、网络智能体及能力评测等方向的研究进展,并结合无线网络用户体验诊断案例,展示大模型与领域知识、专业工具等协同完成复杂任务的应用模式。最后,总结可信闭环、跨场景迁移、评测体系及产业生态等关键挑战。

关键词: 无线网络智能化, 6G, 大模型, 网络智能体

Abstract:

To address the increasingly complex service requirements and operating environments of 6G wireless networks, conventional task-specific models face limitations in cross-task adaptation and domain knowledge integration. Large models, with their capabilities in knowledge representation and task generalization, offer a promising paradigm for advancing wireless network intelligence. This paper reviews recent advances in foundation model development for wireless networks, wireless task applications, network agents, and capability evaluation. Furthermore, through a case study on user experience diagnosis in wireless networks, we demonstrate an application paradigm in which large models collaborate with domain knowledge and specialized tools to complete complex tasks. Finally, this paper discusses key challenges, including trustworthy closed-loop operation, cross-scenario transfer, evaluation frameworks, and the industrial ecosystem.

Key words: wireless network intelligence, 6G, large model, network agent

中图分类号:

TN929.11

李启喆, 孙茜颐, 刘晨吉, 杨武, 朱光旭. 大模型与智能体驱动的6G网络智能化研究现状、应用与展望^*[J]. 信息通信技术与政策, 2026, 52(10): 72-79.

LI Qizhe, SUN Qianyi, LIU Chenji, YANG Wu, ZHU Guangxu. Current status, applications, and prospects of 6G wireless network intelligence driven by large models and agents[J]. Information and Communications Technology and Policy, 2026, 52(10): 72-79.

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链接本文:

http://ictp.caict.ac.cn/CN/10.12267/j.issn.2096-5931.2026.10.009

http://ictp.caict.ac.cn/CN/Y2026/V52/I10/72

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大模型与智能体驱动的6G网络智能化研究现状、应用与展望^*

Current status, applications, and prospects of 6G wireless network intelligence driven by large models and agents

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