基于流形学习的RIS辅助大规模MIMO系统的CSI反馈研究

钱慕君; 虞舜驰; 刘陈; 宋云超; 陆峰

doi:10.11959/j.issn.2096-3750.2024.00374

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基于流形学习的RIS辅助大规模MIMO系统的CSI反馈研究

理论与技术 | 更新时间：2025-01-20

- 基于流形学习的RIS辅助大规模MIMO系统的CSI反馈研究
- Research on CSI feedback of RIS-assisted massive MIMO system based on manifold learning
- 物联网学报 2024年8卷第4期页码：167-176
- 作者机构：
  
  1.南京邮电大学电子与光学工程学院、柔性电子（未来技术）学院，江苏南京 210023
  2.南京邮电大学射频集成与微组装技术国家地方联合工程实验室，江苏南京 210023
- 作者简介：
  
  [ "钱慕君（1986‒ ）女，博士，南京邮电大学电子与光学工程学院、柔性电子（未来技术）学院讲师，主要研究方向为物理层安全技术、无线携能通信、智能反射面等。" ]
  [ "虞舜驰（1998‒ ）男，南京邮电大学电子与光学工程学院、柔性电子（未来技术）学院硕士生，主要研究方向为毫米波大规模MIMO通信、智能反射面等。" ]
  [ "刘陈（1963‒ ）男，博士，南京邮电大学电子与光学工程学院、柔性电子（未来技术）学院教授、博士生导师，主要研究方向为无线通信中的空时信号传输与处理算法、大规模MIMO通信、流形学习与流形优化等。" ]
  [ "宋云超（1988‒ ）男，博士，南京邮电大学电子与光学工程学院、柔性电子（未来技术）学院副教授、硕士生导师，主要研究方向为5G/6G无线通信信号处理。" ]
  [ "陆峰（1978‒ ）男，博士，南京邮电大学电子与光学工程学院、柔性电子（未来技术）学院副教授，主要研究方向为通信信号处理。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62371249;62101282;61901232)
- DOI：10.11959/j.issn.2096-3750.2024.00374
  中图分类号：
- 纸质出版日期：2024-12-10，
  
  收稿日期：2023-07-26，
  
  修回日期：2024-08-19，
- 稿件说明：
移动端阅览
钱慕君, 虞舜驰, 刘陈, 等. 基于流形学习的RIS辅助大规模MIMO系统的CSI反馈研究[J]. 物联网学报, 2024,8(4):167-176.

QIAN MUJUN, YU SHUNCHI, LIU CHEN, et al. Research on CSI feedback of RIS-assisted massive MIMO system based on manifold learning. [J]. Chinese journal on internet of things, 2024, 8(4): 167-176.
钱慕君, 虞舜驰, 刘陈, 等. 基于流形学习的RIS辅助大规模MIMO系统的CSI反馈研究[J]. 物联网学报, 2024,8(4):167-176. DOI： 10.11959/j.issn.2096-3750.2024.00374.

QIAN MUJUN, YU SHUNCHI, LIU CHEN, et al. Research on CSI feedback of RIS-assisted massive MIMO system based on manifold learning. [J]. Chinese journal on internet of things, 2024, 8(4): 167-176. DOI： 10.11959/j.issn.2096-3750.2024.00374.

摘要

针对频分双工（FDD

frequency-division duplexing）模式下可重构智能反射面（RIS

reconfigurable intelligent surface）辅助的多用户大规模多输入多输出（MIMO

multiple-input multiple-output）系统信道反馈开销大的问题，提出了一种基于流形学习的信道状态信息（CSI

channel state information）反馈框架。该框架首先通过简化CSI反馈过程实现初步的反馈开销降低，然后结合流形学习思想训练两组字典，从而实现增量CSI的降维和重构，最后在基站端恢复原始信道。仿真结果表明，在多用户和有限散射环境下，所提的CSI反馈方案与现有的方法相比具有更低的开销和复杂度，而且重构质量得到显著提高。

Abstract

To solve the problem of high feedback overhead in a multi-user massive multiple-input multiple-output (MIMO) system assisted by a reconfigurable intelligent surface (RIS) in frequency-division duplexing (FDD) mode

a channel state information (CSI) feedback framework based on manifold learning was proposed. Firstly

the framework achieved initial feedback overhead reduction by simplifying the CSI feedback process. Then

the framework combined the manifold learning to train two set of dictionaries to achieve dimension reduction and reconstruction of incremental CSI. Finally

the original channel was restored at the base station. The simulation results show that the CSI feedback scheme proposed in this paper has lower overhead and complexity than the existing methods in the multi-user and limited scattering environment

and the reconstruction quality is significantly improved.

关键词

大规模MIMO频分双工智能反射面信道反馈流形学习

Keywords

massive MIMOFDDRISchannel feedbackmanifold learning

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基于深度学习的大规模MIMO信道状态信息反馈

一种基于深度学习的物联网信道状态信息获取算法