Research on CSI feedback of RIS-assisted massive MIMO system based on manifold learning

QIAN Mujun; YU Shunchi; LIU Chen; SONG Yunchao; LU Feng

doi:10.11959/j.issn.2096-3750.2024.00374

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Research on CSI feedback of RIS-assisted massive MIMO system based on manifold learning

Theory and Technology | 更新时间：2025-01-20

- Research on CSI feedback of RIS-assisted massive MIMO system based on manifold learning
- Chinese Journal on Internet of Things Vol. 8, Issue 4, Pages: 167-176(2024)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(62371249;62101282;61901232)
- DOI：10.11959/j.issn.2096-3750.2024.00374
  CLC：
- Received：26 July 2023，
  
  Revised：2024-08-19，
  
  Published：10 December 2024
- 稿件说明：
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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,2024,08(04):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,2024,08(04):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,2024,08(04):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,2024,08(04):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

Keywords

references

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