面向B5G/6G的三三三网络体系架构和优化学习机制

朱近康; 柴名扬; 周武旸

doi:10.11959/j.issn.1000-436x.2021095

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面向B5G/6G的三三三网络体系架构和优化学习机制

专题：面向未来移动网络的大规模组网关键技术 | 更新时间：2024-06-05

- 面向B5G/6G的三三三网络体系架构和优化学习机制
- Three-three-three network architecture and learning optimization mechanism for B5G/6G
- 通信学报 2021年42卷第4期页码：62-75
- 作者机构：
  
  1. 中国科学技术大学信息科学技术学院，安徽合肥 230027
  2. 中国科学院无线光通信重点实验室，安徽合肥 230027
- 作者简介：
  
  [ "朱近康（1943- ），男，四川内江人，中国科学技术大学教授、博士生导师，主要研究方向为无线通信理论、技术和网络的研究、无线大数据和无线 AI、绿色无线、5G/6G的创新技术。" ]
  [ "柴名扬（1996- ），男，河南开封人，中国科学技术大学硕士生，主要研究方向为无线大数据、无线AI和混合预编码。" ]
  [ "周武旸（1972- ），男，安徽合肥人，博士，中国科学技术大学教授、博士生导师，主要研究方向为移动通信与无线通信网、卫星移动通信等。" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2018YFB1801105);国家自然科学基金资助项目(61631018)
- DOI：10.11959/j.issn.1000-436x.2021095
  中图分类号： TN915.5
- 网络出版日期：2021-04，
  
  纸质出版日期：2021-04-25
- 稿件说明：
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朱近康, 柴名扬, 周武旸. 面向B5G/6G的三三三网络体系架构和优化学习机制[J]. 通信学报, 2021,42(4):62-75.

Jinkang ZHU, Mingyang CHAI, Wuyang ZHOU. Three-three-three network architecture and learning optimization mechanism for B5G/6G[J]. Journal on communications, 2021, 42(4): 62-75.
朱近康, 柴名扬, 周武旸. 面向B5G/6G的三三三网络体系架构和优化学习机制[J]. 通信学报, 2021,42(4):62-75. DOI： 10.11959/j.issn.1000-436x.2021095.

Jinkang ZHU, Mingyang CHAI, Wuyang ZHOU. Three-three-three network architecture and learning optimization mechanism for B5G/6G[J]. Journal on communications, 2021, 42(4): 62-75. DOI： 10.11959/j.issn.1000-436x.2021095.

摘要

面对未来B5G/6G网络是大连接复杂智能网络，极其大量的用户连接、需求连接、服务连接，加之3G、4G、5G 甚至 6G 的综合运用，将导致网络变得极其复杂的挑战，提出了一种三三三网络体系架构，它是一个包含3类网络（核心网、接入网、终端网）、3种资源（频率带宽、功耗、时延）和3项需求（生活、工作、服务）的三维立体综合优化的体系架构，简称三三三网络。进而，论证了三维立体复杂体系的数学计算式，给出了知识+数据驱动学习模型和利用知识学习机制进行智能处理的优化方法。最后，给出了三三三网络的数值例和可达性能。研究结果表明，所提三三三网络体系架构和优化方法对于设计和运行未来的大连接复杂智能网络是有益的。

Abstract

Aiming at the problem that the future B5G/6G network is a complex intelligent network with large connections

coupled with the comprehensive application of 3G

5G and even 6G

the future networks will inevitably become extremely complex

a three-three-three network architecture was proposed that was a network that includes three types of networks (core network

access network and terminal network)

three resources (frequency band

power and time consumptions) and three requirements (active

work and service)

which was a three-dimensional comprehensive optimization system architecture

referred to as the three-three-three network.Furthermore

the mathematical basic formulas of the three-dimensional complex network were analyzed

the knowledge + data-driven learning model and the optimization method of intelligent processing using the knowledge learning mechanism were presented.Finally

the numerical example and reachable performance of the three-three-three network were given.Those results demonstrate that the proposed network architecture and the learning optimization mechanism are beneficial for designing future large-connected complex intelligent networks.

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Keywords

references

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