基于张量分解和流形优化的双IRS辅助毫米波MIMO系统信道估计

冯凯辉; 刘陈; 黄钲; 宋云超; 高润勤

doi:10.11959/j.issn.2096-3750.2024.00378

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基于张量分解和流形优化的双IRS辅助毫米波MIMO系统信道估计

理论与技术 | 更新时间：2025-01-20

- 基于张量分解和流形优化的双IRS辅助毫米波MIMO系统信道估计
- Channel estimation for double IRS-assisted millimeter wave MIMO systems based on tensor decomposition and manifold optimization
- 物联网学报 2024年8卷第4期页码：119-128
- 作者机构：
  
  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
- 作者简介：
  
  [ "冯凯辉（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MIMO系统信道估计、流形优化等。" ]
  [ "刘陈（1963‒ ），男，博士，南京邮电大学教授、博士生导师，主要研究方向为无线通信中的信号处理、空时信号传输与处理算法、协作通信与干扰对齐方法。" ]
  [ "黄钲（1997‒ ），男，南京邮电大学电子与光学工程学院、柔性电子（未来技术）学院博士生，主要研究方向为无线通信信号处理、流形优化。" ]
  [ "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。" ]
  [ "高润勤（1999‒ ），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MIMO系统信道估计、流形优化等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62101282)
- DOI：10.11959/j.issn.2096-3750.2024.00378
  中图分类号：
- 纸质出版日期：2024-12-10，
  
  收稿日期：2023-05-26，
  
  修回日期：2024-08-19，
- 稿件说明：
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冯凯辉, 刘陈, 黄钲, 等. 基于张量分解和流形优化的双IRS辅助毫米波MIMO系统信道估计[J]. 物联网学报, 2024,8(4):119-128.

FENG KAIHUI, LIU CHEN, HUANG ZHENG, et al. Channel estimation for double IRS-assisted millimeter wave MIMO systems based on tensor decomposition and manifold optimization. [J]. Chinese journal on internet of things, 2024, 8(4): 119-128.
冯凯辉, 刘陈, 黄钲, 等. 基于张量分解和流形优化的双IRS辅助毫米波MIMO系统信道估计[J]. 物联网学报, 2024,8(4):119-128. DOI： 10.11959/j.issn.2096-3750.2024.00378.

FENG KAIHUI, LIU CHEN, HUANG ZHENG, et al. Channel estimation for double IRS-assisted millimeter wave MIMO systems based on tensor decomposition and manifold optimization. [J]. Chinese journal on internet of things, 2024, 8(4): 119-128. DOI： 10.11959/j.issn.2096-3750.2024.00378.

摘要

针对双智能反射面（IRS

intelligent reflecting surface）辅助毫米波多入多出（MIMO

multiple-input multiple-output）系统信道估计问题，提出一种基于张量分解和流形优化的信道估计方案。首先，利用接收信号的高维特征构建张量模型，并基于张量的Tucker2分解形式给出信道估计问题的目标函数；其次，利用交替优化理论将信道估计问题拆分为多个子问题，为双IRS场景下用户与IRS、双IRS之间和IRS与基站之间信道的分别估计提供了可行方案；最后，考虑毫米波信道本身的低秩特性，将各个信道估计子问题转化为在复定秩矩阵流形上的优化问题，利用复定秩流形优化求解秩受限优化问题的优势，提出基于流形优化的交替信道估计方案。不同于传统方案，所提方案考虑了毫米波信道的低秩特性，对信道进行了准确描述，并应用流形优化理论有效处理定秩约束，提高了信道估计精度。仿真结果表明，在不同场景下所提方案的估计性能均优于现有参考方案。

Abstract

The issue of channel estimation for a double intelligent reflecting surface (IRS) assisted millimeter wave multiple-input multiple-output (MIMO) system was addressed and a channel estimation scheme based on tensor decomposition and manifold optimization was proposed. Specifically

a tensor model was constructed based on the high-dimensional features of received signals

and the objective function of the channel estimation problem was formulated based on the Tucker2 decomposition of the tensor. Then

the channel estimation problem was decomposed into multiple sub-problems using alternating optimization theory

providing feasible solutions for estimating the channel of each hop in the double IRS scenario. Finally

considering the low-rank characteristics of the millimeter wave channel itself

each channel estimation sub-problem was transformed into an optimization problem on the complex fixed-rank matrix manifold

and a manifold optimization-based alternating channel estimation scheme was proposed by leveraging the advantages of fixed-rank manifold optimization in solving rank-constrained optimization problems. Unlike traditional schemes

the proposed scheme takes into account the low-rank characteristics of millimeter wave channels

accurately describes the channels

and effectively handles fixed-rank constraints using manifold optimization theory

thus improving the accuracy of channel estimation. Simulation results show that the proposed channel estimation scheme outperforms existing reference schemes in terms of estimation performance in different scenarios.

关键词

毫米波MIMO系统双智能反射面信道估计流形优化张量分解

Keywords

millimeter wave MIMO systemdouble IRSchannel estimationmanifold optimizationtensor decomposition

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