Endogenous Intelligent Agent Architecture and Intent-Driven Subnetwork Generation Mechanism for 6G Core Networks

LI Ziyan; GU Xiaofei; LI Huixin; WANG Yapeng; AI Ming

doi:10.11959/j.issn.1000-0801.DXKX260132

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Endogenous Intelligent Agent Architecture and Intent-Driven Subnetwork Generation Mechanism for 6G Core Networks

更新时间：2026-04-09

- Endogenous Intelligent Agent Architecture and Intent-Driven Subnetwork Generation Mechanism for 6G Core Networks
- Telecommunications Science (2026)
- 作者机构：
  
  1.中信科移动通信技术股份有限公司创新中心，北京 100085
  2.无线移动通信国家重点实验室（电信科学技术研究院有限公司），北京，100191
  3.大唐移动通信设备有限公司，北京，100083
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.DXKX260132
  CLC： TN929.5;TP18
- Received：28 February 2026，
  
  Revised：2026-03-20，
  
  Accepted：09 April 2026，
- 稿件说明：
移动端阅览
LI Ziyan, GU Xiaofei, LI Huixin, et al. Endogenous Intelligent Agent Architecture and Intent-Driven Subnetwork Generation Mechanism for 6G Core Networks[J/OL]. Telecommunications Science, 2026.
DOI：

LI Ziyan, GU Xiaofei, LI Huixin, et al. Endogenous Intelligent Agent Architecture and Intent-Driven Subnetwork Generation Mechanism for 6G Core Networks[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260132.

摘要

为实现IMT-2030（6G）普惠智能愿景，提出一种内生智能（AI-Native）核心网架构，该架构具有多智能体协同特征。基于服务化架构，通过定义意图受理（AIEF）、智能控制（ACF）与资源管理网络功能（ARMF）等核心网络功能，构建了面向6G场景的内生智能网络架构。为验证架构可行性，选取意图驱动子网生成任务作为典型任务，搭建整体系统验证架构所提注册、编排、UE接入基本功能。针对6G逻辑子网构建需求，设计了由ACF全程协调保障的两阶段多智能体协同的子网生成机制，并在单卡RTX4090D下开展仿真实验与性能评估工作。在意图解析阶段，利用大模型常识推理能力将非结构化意图解析收敛为标准化策略契约，推理时延平均为1744.9ms；在子网规划与生成阶段，由两个智能体协同推理完成子网自动化规划与生成，子网生成时延平均为185.7ms，1000条不同意图的子网生成成功率为96.6%。验ss证表明，所提架构与机制可实现基于自然语言的意图表述到子网实例的生成闭环，验证了未来网络引入AI智能体实现内生的可行性。

Abstract

To realize the IMT‑2030 (6G) vision of ubiquitous intelligence

this paper proposes an AI‑native core network architecture featuring multi‑agent collaboration. Built upon a service‑based framework

the architecture introduces three core network functions: Intent Awareness (AIEF)

Intelligent Control (ACF)

and Resource Management (ARMF). To validate the architecture

an intent‑driven subnet generation task is selected as a representative use case

and a two‑stage multi‑agent collaborative mechanism coordinated by ACF is designed. Experiments conducted on a single RTX 4090D show that in the intent parsing stage

a large language model leverages commonsense reasoning to convert unstructured intents into standardized policy contracts

achieving an average inference latency of 1744.9 ms. In the subsequent subnet planning and generation stage

two AI agents collaboratively automate the subnet generation process

with an average latency of 185.7 ms. Across 1

000 diverse intents

the subnet generation success rate reaches 96.6%. The results confirm that the proposed architecture enables a closed loop from natural-language intent to subnet instance generation

validating the feasibility of embedding AI agents as native components in future networks.

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references

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