Intelligent radio access network for 5G-Advanced and 6G: key standard technologies and future evolution

Zhao Zhe; Chen Jiajun; Gao Yin

doi:10.11959/j.issn.1000-0801.2026029

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Intelligent radio access network for 5G-Advanced and 6G: key standard technologies and future evolution

Research and Development | 更新时间：2026-03-02

- Intelligent radio access network for 5G-Advanced and 6G: key standard technologies and future evolution
- Telecommunications Science Vol. 42, Issue 1, Pages: 105-115(2026)
- 作者机构：
  
  1.移动网络和移动多媒体技术国家重点实验室，广东深圳 518055
  2.中兴通讯股份有限公司，广东深圳 518057
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2026029
  CLC： TP393;TN929.5
- Received：29 September 2025，
  
  Revised：2025-10-14，
  
  Accepted：20 October 2025，
  
  Published：20 January 2026
- 稿件说明：
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赵喆,陈嘉君,高音.面向5G-Advanced与6G的智能无线电接入网：关键标准技术与未来演进[J].电信科学,2026,42(01):105-115.

Zhao Zhe,Chen Jiajun,Gao Yin.Intelligent radio access network for 5G-Advanced and 6G: key standard technologies and future evolution[J].Telecommunications Science,2026,42(01):105-115.
赵喆,陈嘉君,高音.面向5G-Advanced与6G的智能无线电接入网：关键标准技术与未来演进[J].电信科学,2026,42(01):105-115. DOI： 10.11959/j.issn.1000-0801.2026029.

Zhao Zhe,Chen Jiajun,Gao Yin.Intelligent radio access network for 5G-Advanced and 6G: key standard technologies and future evolution[J].Telecommunications Science,2026,42(01):105-115. DOI： 10.11959/j.issn.1000-0801.2026029.

摘要

5G/5G-Advanced在持续提升关键性能指标方面被寄予厚望，需要在时延、可靠性、连接数密度与用户体验等方面实现进一步突破。传统以人工操作为主的管理模式在效率、准确性与成本等方面的局限日益凸显。相较于传统优化方法，人工智能技术凭借其预测性与前瞻性，推动网络管理由被动应对转向主动感知与自优化，实现从“监测-响应”到“预判-编排”的迁移。基于3GPP在无线电接入网（radio access network，RAN）智能化方向的关键技术与标准化路径，结合典型用例场景，分析了AI/ML模型管理、数据采集与交互机制。面向6G智能RAN，进一步提出“意图驱动的协作任务”这一新型架构理念，其关键是通过RAN对应用层信息的感知、任务级别的服务质量（quality of service，QoS）监控、动态组和资源管理等技术实现6G网络人机及碳硅生态系统的无缝交互。

Abstract

5G/5G-Advanced is expected to continuously enhance key performance indicators

requiring further breakthroughs in aspects such as latency

reliability

connection density

and user experience. The limitations of traditional human-computer interactive network management

which relies primarily on manual operations

are becoming increasingly evident in terms of efficiency

accuracy

and cost. Compared to traditional optimization methods

the predictive and forward-looking capabilities of AI/ML enable the network to shift from passive response to active perception and self-optimization

achieving a transition from “monitoring-reaction” to “prediction-orchestration”. Based on the key technologies and standardization paths for radio access network (RAN) intelligence defined by 3GPP

the AI/ML model management

data collection

and interaction mechanisms were analyzed in conjunction with typical use case scenarios. For intelligent RAN in 6G

a novel architectural concept termed the “intent-driven collaborative task”was proposed. Its key implementation relied on RAN's awareness of application-layer information

task-level quality of service (QoS) monitoring

dynamic grouping and resource management

and other technologies to achieve seamless interaction between human

machine

and the carbon-silicon ecosystem in 6G networks.

关键词

Keywords

references

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