Research on the key technologies of edge intelligent for real-time communication

WANG Chen; BAI Xueqian; WEI Bin; SONG Yue; ZHANG Qiang

doi:10.11959/j.issn.1000-0801.2025194

您当前的位置：

首页 >

文章列表页 >

Research on the key technologies of edge intelligent for real-time communication

Topic: AI-Empowered Communication Networks | 更新时间：2026-01-20

- Research on the key technologies of edge intelligent for real-time communication
- Telecommunications Science Vol. 41, Issue 11, Pages: 14-30(2025)
- 作者机构：
  
  1.中国移动通信有限公司研究院，北京 100053
  2.中兴通讯股份有限公司，广东深圳 518063
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2025194
  CLC： TN915.81
- Received：13 June 2025，
  
  Revised：2025-07-22，
  
  Accepted：28 July 2025，
  
  Published：20 November 2025
- 稿件说明：
移动端阅览
王辰,白雪茜,魏彬等.面向实时通信的边缘智能关键技术研究[J].电信科学,2025,41(11):14-30.

WANG Chen,BAI Xueqian,WEI Bin,et al.Research on the key technologies of edge intelligent for real-time communication[J].Telecommunications Science,2025,41(11):14-30.
王辰,白雪茜,魏彬等.面向实时通信的边缘智能关键技术研究[J].电信科学,2025,41(11):14-30. DOI： 10.11959/j.issn.1000-0801.2025194.

WANG Chen,BAI Xueqian,WEI Bin,et al.Research on the key technologies of edge intelligent for real-time communication[J].Telecommunications Science,2025,41(11):14-30. DOI： 10.11959/j.issn.1000-0801.2025194.

摘要

针对当前实时通信网络架构复杂、新业务引入缓慢等问题，结合未来实时通信智能化的演进趋势，提出了智能内生实时通信网络边缘智能架构，包含统一控制面、统一智能面、边缘敏捷媒体面等，以支撑传统实时通信网络从“通话入口”向“应用入口”“超级入口”的演进；研究了网络人工智能（artificial intelligence，AI）内生体系，提出边缘智能基本策略、单智能体通信架构；分析了实时通信边云协同推理关键技术，提出了分布式AI模型管理、边云协同推理机制和构建用户知识图谱。

Abstract

In view of the pain points faced by the complex architecture and slow introduction of new services of the current real-time communication network

and the future evolution trend of intelligent real-time communication

an edge intelligent architecture of intelligent agile real-time communication network was proposed

including an unified control plane

a unified intelligent plane and an edge agile media plane

so that the intelligent communication network can evolve from “call entrance” to “application entrance” and “super entrance”. The intrinsic network artificial intelligence(AI) system was studied

and the basic strategy of edge intelligence and the communication architecture of a single AI agent were proposed. The key technologies of collaborative inference of edge-cloud for real-time communication were analyzed. The construction of distributed AI model management

collaborative inference mechanisms of end-edge-cloud and user’s knowledge maps were proposed.

关键词

Keywords

references

LIN X Q . 3GPP evolution from 5G to 6G: a 10-year retrospective [J ] . Telecom , 2025 , 6 ( 2 ): 32 .

XU F M , ZHANG L Y , ZHOU Z . Interworking of wimax and 3GPP networks based on IMS [IP multimedia systems (IMS) infrastructure and services [J ] . IEEE Communications Magazine , 2007 , 45 ( 3 ): 144 - 150 .

金宁 , 王庆扬 . 基于聚类算法的6G典型应用场景研究 [J ] . 电信科学 , 2022 , 38 ( 1 ): 121 - 131 .

JIN N , WANG Q Y . Research on clustering algorithm based 6G typical usage scenarios [J ] . Telecommunications Science , 2022 , 38 ( 1 ): 121 - 131 .

BESSIS T . Improving performance and reliability of an IMS network by co-locating IMS servers [J ] . Bell Labs Technical Journal , 2006 , 10 ( 4 ): 167 - 178 .

翟振辉 , 郝倩 , 刘蕾 , 等 . 下一代新通话网络媒体面演进及关键技术研究 [J ] . 电信科学 , 2025 , 41 ( 4 ): 191 - 198 .

ZHAI Z H , HAO Q , LIU L , et al . Research on the evolution and key technology of the next generation new calling network media node [J ] . Telecommunications Science , 2025 , 41 ( 4 ): 191 - 198 .

杨震 , 赵建军 , 黄勇军 , 等 . 基于网络演进的人工智能技术方向研究 [J ] . 电信科学 , 2022 , 38 ( 12 ): 27 - 34 .

YANG Z , ZHAO J J , HUANG Y J , et al . Study on the direction of artificial intelligence technology based on network evolution [J ] . Telecommunications Science , 2022 , 38 ( 12 ): 27 - 34 .

唐博恒 , 柴鑫刚 . 基于云边协同的计算机视觉推理机制 [J ] . 电信科学 , 2021 , 37 ( 5 ): 72 - 81 .

TANG B H , CHAI X G . Cloud-edge collaboration based computer vision inference mechanism [J ] . Telecommunications Science , 2021 , 37 ( 5 ): 72 - 81 .

陈新宇 , 牛娇红 , 陆光辉 . 基于6G AIaaS的分布式网络框架及技术方案 [J ] . 移动通信 , 2023 , 47 ( 6 ): 110 - 114 .

CHEN X Y , NIU J H , LU G H . Distributed network framework and technical solution based on 6G AIaaS [J ] . Mobile Communications , 2023 , 47 ( 6 ): 110 - 114 .

王晴天 , 刘洋 , 刘海涛 , 等 . 面向6G的网络智能化研究 [J ] . 电信科学 , 2022 , 38 ( 9 ): 151 - 160 .

WANG Q T , LIU Y , LIU H T , et al . Research on network intelligence for 6G [J ] . Telecommunications Science , 2022 , 38 ( 9 ): 151 - 160 .

李琴 , 李唯源 , 孙晓文 , 等 . 6G网络智能内生的思考 [J ] . 电信科学 , 2021 , 37 ( 9 ): 20 - 29 .

LI Q , LI W Y , SUN X W , et al . Thinking of native artificial intelligence in 6G networks [J ] . Telecommunications Science , 2021 , 37 ( 9 ): 20 - 29 .

STRINATI E C , DI LORENZO P , SCIANCALEPORE V , et al . Goal-oriented and semantic communication in 6G AI-native networks: the 6G-GOALS approach [C ] // Proceedings of the 2024 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) . Piscataway : IEEE Press , 2024 : 1 - 6 .

侯祥鹏 , 兰兰 , 陶长乐 , 等 . 边缘智能与协同计算: 前沿与进展 [J ] . 控制与决策 , 2024 , 39 ( 7 ): 2385 - 2404 .

HOU X P , LAN L , TAO C L , et al . Edge intelligence and collaborative computing: frontiers and advances [J ] . Control and Decision , 2024 , 39 ( 7 ): 2385 - 2404 .

TIAN Y Q , ZHANG Z Y , YANG Y Z , et al . An edge-cloud collaboration framework for generative AI service provision with synergetic big cloud model and small edge models [J ] . IEEE Network , 2024 , 38 ( 5 ): 37 - 46 .

YAO J C , ZHANG S Y , YAO Y , et al . Edge-cloud polarization and collaboration: a comprehensive survey for AI [J ] . IEEE Transactions on Knowledge and Data Engineering , 2023 , 35 ( 7 ): 6866 - 6886 .

YANG Z , YANG Y , ZHAO C , et al . PerLLM: personalized inference scheduling with edge-cloud collaboration for diverse LLM services [J ] . arXiv preprint , 2024 , arXiv: 2405.14636 .

LI M , LI Y , TIAN Y , et al . AppealNet: an efficient and highly-accurate edge/cloud collaborative architecture for DNN inference [C ] // Proceedings of the 2021 58th ACM/IEEE Design Automation Conference (DAC) . Piscataway : IEEE Press , 2021 : 409 - 414 .

KARACHALIOS O A , ZAFEIROPOULOS A , KONTOVASILIS K , et al . Distributed machine learning and native AI enablers for end-to-end resources management in 6G [J ] . Electronics , 2023 , 12 ( 18 ): 3761 .

TEERAPITTAYANON S , MCDANEL B , KUNG H T . Distributed deep neural networks over the cloud, the edge and end devices [C ] // Proceedings of the 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) . Piscataway : IEEE Press , 2017 : 328 - 339 .

YUAN Y L , JIAO L , ZHU K L , et al . AI in 5G: the case of online distributed transfer learning over edge networks [C ] // Proceedings of the IEEE INFOCOM 2022-IEEE Conference on Computer Communications . Piscataway : IEEE Press , 2022 : 810 - 819 .

张天成 , 田雪 , 孙相会 , 等 . 知识图谱嵌入技术研究综述 [J ] . 软件学报 , 2023 , 34 ( 1 ): 277 - 311 .

ZHANG T C , TIAN X , SUN X H , et al . Overview on knowledge graph embedding technology research [J ] . Journal of Software , 2023 , 34 ( 1 ): 277 - 311 .

Views

4591

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on RTCWeb and Its Merging with IMS

Research on Private Network Architecture and Key Technologies for Enterprise Multi-Agents Collaboration

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

Related Author

Lifeng Le

Jin Peng

Xiaodong Duan

HAN Zhengxin

HE Tao

HAN Mengyao

PANG Ran

CAO Chang

Related Institution

China Unicom Research Institute

Innovation Center, CICT Mobile Communication Technology Co., Ltd.

State Key Lab of Wireless Mobile Communication, China Academy of Telecommunications Technology

Datang Mobile Communications Equipment Co., Ltd

⁰