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可重构智能超表面辅助的大规模机器类通信深度学习大规模MIMO信道估计

刘婷 王媛 辛元雪

刘婷, 王媛, 辛元雪. 可重构智能超表面辅助的大规模机器类通信深度学习大规模MIMO信道估计[J]. 电子与信息学报. doi: 10.11999/JEIT240584
引用本文: 刘婷, 王媛, 辛元雪. 可重构智能超表面辅助的大规模机器类通信深度学习大规模MIMO信道估计[J]. 电子与信息学报. doi: 10.11999/JEIT240584
LIU Ting, WANG Yuan, XIN Yuanxue. Deep Learning-enhanced Massive Channel Estimation for Reconfigurable Intelligent Surface-aided Massive Machine-Type Communication[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240584
Citation: LIU Ting, WANG Yuan, XIN Yuanxue. Deep Learning-enhanced Massive Channel Estimation for Reconfigurable Intelligent Surface-aided Massive Machine-Type Communication[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240584

可重构智能超表面辅助的大规模机器类通信深度学习大规模MIMO信道估计

doi: 10.11999/JEIT240584
基金项目: 国家自然科学基金 (62101274),江苏省自然科学基金 (BK20210640)
详细信息
    作者简介:

    刘婷:女,讲师,研究方向为超大规模连接无线传输技术

    王媛:女,硕士生,研究方向为无线通信

    辛元雪:女,副教授,研究方向为大规模MIMO频谱效率、能量效率和新型双工技术

    通讯作者:

    刘婷 liuting@nuist.edu.cn

  • 中图分类号: TN929.5

Deep Learning-enhanced Massive Channel Estimation for Reconfigurable Intelligent Surface-aided Massive Machine-Type Communication

Funds: The National Natural Science Foundation of China (62101274), The Natural Science Foundation of Jiangsu Province (BK20210640)
  • 摘要: 大规模机器类通信 (mMTC) 是第5代移动通信系统的重要应用场景之一,可实现每平方公里近百万级设备的连接。考虑到mMTC传播环境的复杂性,该文引入可重构智能超表面 (RIS) 进行上行免授权的传输,由此级联形成用户与RIS、RIS与基站 (BS) 之间的信道链路,从而有效控制无线信号传输的质量。在此基础上,建立Turbo译码消息传递思想下的降噪学习系统,通过大量的训练数据,以学习RIS辅助的级联信道状态信息,并对其进行估计。此外,该文对RIS辅助的mMTC信道估计结果进行了统计分析,以验证所提方案的准确性。数值仿真结果和理论分析结果表明,该文方法优于其他压缩感知类的方法。
  • 图  1  RIS辅助的mMTC上行传输系统示意图

    图  2  信道估计深度学习架构图

    图  3  联合GAN和DnCNN的降噪模块图

    图  4  不同系统模型下的信道估计性能比较,$ M = 32 $

    图  5  RIS辅助系统的MSE性能比较,$ M = 64 $

    图  6  不同RIS单元数量下的MSE性能比较,$ M = 64 $

    图  7  不同学习层数下的MSE性能比较

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出版历程
  • 收稿日期:  2024-07-09
  • 修回日期:  2024-09-18
  • 网络出版日期:  2024-09-24

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