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面向6G全域融合的智能接入关键技术综述

王雪 孟姝宇 钱志鸿

王雪, 孟姝宇, 钱志鸿. 面向6G全域融合的智能接入关键技术综述[J]. 电子与信息学报. doi: 10.11999/JEIT231224
引用本文: 王雪, 孟姝宇, 钱志鸿. 面向6G全域融合的智能接入关键技术综述[J]. 电子与信息学报. doi: 10.11999/JEIT231224
WANG Xue, MENG Shuyu, QIAN Zhihong. An Overview of Key Technologies for Intelligent Access Toward 6G Full-domain Convergence[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231224
Citation: WANG Xue, MENG Shuyu, QIAN Zhihong. An Overview of Key Technologies for Intelligent Access Toward 6G Full-domain Convergence[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231224

面向6G全域融合的智能接入关键技术综述

doi: 10.11999/JEIT231224
基金项目: 国家自然科学基金(62171198)
详细信息
    作者简介:

    王雪:女,教授,研究方向为物联网、D2D通信技术与异构无线网络等

    孟姝宇:女,博士生,研究方向为非正交多址接入技术

    钱志鸿:男,教授,研究方向为物联网、D2D、Wi-Fi、RFID等无线网络与通信技术

    通讯作者:

    钱志鸿 dr.qzh@163.com

  • 中图分类号: TN92

An Overview of Key Technologies for Intelligent Access Toward 6G Full-domain Convergence

Funds: The National Natural Science Foundation of China (62171198)
  • 摘要: 针对空天地一体化接入网络,该文在总结相关研究的基础上,阐述了未来空天地一体化接入架构的关键技术,分析了空口技术、多址技术、干扰分析、计算技术和人工智能(Artificial Intelligence, AI)技术等几个重点方向的研究进展,提出了多种接入形式并存的灵活性网络架构。针对6G全域融合网络接入的重点研究问题,结合用户的服务质量需求,构建了一体化AI赋能架构,提出了大规模混合多址接入及弹性资源适配策略。基于网络架构立体化、网络协同传输、一体化网络资源管理、未来空天地接入技术以及网络协同计算等未来重点研究方向进行了讨论和展望。
  • 图  1  研究背景

    图  2  星地统一空口设计思路

    图  3  卫星对地面系统产生干扰

    图  4  6G卫星通信网络接入架构

    图  5  SAGIN接入架构中的AI赋能应用

    图  6  星地频率共享与干扰规避

    表  1  空口可变参数集的主要参数

    主要参数 参考范围 备注
    信道带宽 180 kHz~1 GHz 适应物联网、带宽传输等多类型业务需求
    调制波形 循环前缀-正交频分复用(Cyclic Prefix-Orthogonal Frequency Division Multiplexing, CP-OFDM) 单载波波形/OFDM
    编码方式 Polar码、卷积码等 可支持其他类型编码
    子载波间隔 15 kHz, 60 kHz等 提供多种子载波间隔
    多址接入方式 正交频分多址接入(Orthogonal Frequency Division Multiple Access, OFDMA),图样分割非正交多址接入(Pattern Division Multiple Access, PDMA)等 根据不同场景选取不同多址接入方式
    多波束协同传输 支持多场景波束联合传输 发挥多波束联合分集增益,提高系统容量
    随机接入方式 随遇、极简接入 根据业务需求、网络状态选择接入
    切换方式 极智切换 支持基于位置、终端需求的切换方式
    下载: 导出CSV

    表  2  AI技术研究现状

    智能化内容/目标AI方法代表文献
    自适应多路径路由发现深度Q网络(Deep Q-Network, DQN)、深度确定性策略梯度
    (Deep Deterministic Policy Gradient, DDPG)算法
    [55]
    信道资源调度智能水滴算法[56]
    卫星信道中信号失真问题强化学习(Reinforcement Learning, RL)[57]
    多波束卫星资源分配多目标强化和自适应神经网络[58]
    6G卫星通信网络AI赋能技术[59]
    多层卫星通信智能改进的深度强化学习(Deep Reinforcement Learning, DRL)算法[60]
    多波束卫星动态资源分配DRL[61]
    降低带宽下行链路成本深度神经网络(Deep Neural Networks, DNN)[62]
    下载: 导出CSV
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  • 收稿日期:  2023-11-03
  • 修回日期:  2024-01-24
  • 网络出版日期:  2024-01-31

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