基于有源器件部署的半有源IRS系统多层采样超分辨率信道估计

郑明坤; 刘天乐; 潘鹏; 郑雅文; 聂柏润; 何腾蛟

doi:10.11959/j.issn.1000-0801.071

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基于有源器件部署的半有源IRS系统多层采样超分辨率信道估计

更新时间：2026-03-02

- 基于有源器件部署的半有源IRS系统多层采样超分辨率信道估计
- Deployment-Oriented Multi-Layer Sampling-Based Super-Resolution Channel Estimation for Semi-Passive IRS Systems
- 电信科学 2026年
- 作者机构：
  
  1.杭州电子科技大学通信工程学院，浙江杭州 310018
  2.北京跟踪与通信技术研究所，北京 100094
  3.中国电子科技集团公司第五十研究所，上海 200331
  4.暨南大学信息科学技术学院，广东广州 510632
- 作者简介：
  
  [ "郑明坤（2001−），男，杭州电子科技大学通信工程学院硕士生，主要研究方向为智能反射面通信系统。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.071
  中图分类号： TN929.5
- 收稿：2026-01-12，
  
  修回：2026-02-01，
  
  录用：2026-02-28，
- 稿件说明：
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郑明坤, 刘天乐, 潘鹏, 等. 基于有源器件部署的半有源IRS系统多层采样超分辨率信道估计[J/OL]. 电信科学, 2026.

ZHENG Mingkun, LIU Tianle, PAN Peng, et al. Deployment-Oriented Multi-Layer Sampling-Based Super-Resolution Channel Estimation for Semi-Passive IRS Systems[J/OL]. Telecommunications Science, 2026.
郑明坤, 刘天乐, 潘鹏, 等. 基于有源器件部署的半有源IRS系统多层采样超分辨率信道估计[J/OL]. 电信科学, 2026. DOI： 10.11959/j.issn.1000-0801.071.

ZHENG Mingkun, LIU Tianle, PAN Peng, et al. Deployment-Oriented Multi-Layer Sampling-Based Super-Resolution Channel Estimation for Semi-Passive IRS Systems[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.071.

摘要

通过在传统智能反射面（IRS）中引入少量有源器件，半有源IRS可缓解信道估计挑战，从而增强IRS的工程可部署性。然而，现有研究尚未系统地考虑有源器件部署策略对信道估计的影响，使得在实际应用中难以平衡估计精度、有源器件数量、算法复杂度和导频开销之间的矛盾。针对上述问题，本文利用信道矩阵的低秩先验及其协方差矩阵的Toeplitz结构特性，提出一种面向半有源IRS的低导频、低复杂度超分辨率信道估计方案。仿真结果表明，本方案通过利用低秩矩阵补全理论与Toeplitz矩阵的结构特点设计采样方案，有针对性地配置有源器件的位置和数量，缓解导频开销问题，实现信道参数的超分辨率估计，并通过一系列低复杂度算法显著降低现有方案复杂度。

Abstract

By introducing a small number of active elements into conventional intelligent reflecting surfaces (IRSs)

semi-passive IRSs can effectively alleviate the challenges of channel estimation

thereby enhancing the practical deployability of IRSs. However

existing studies have not systematically investigated the impact of active element deployment strategies on channel estimation

which makes it difficult in practice to jointly balance estimation accuracy

the number of active elements

algorithmic complexity

and pilot overhead. To address this issue

this paper exploits the low-rank prior of the channel matrix together with the Toeplitz structure of its covariance matrix

and proposes a low-pilot and low-complexity super-resolution channel estimation scheme for semi-passive IRS systems. Numerical results demonstrate that

by leveraging low-rank matrix completion theory and the structural properties of Toeplitz matrices in the sampling design

the proposed scheme can selectively configure the locations and number of active elements

thereby reducing pilot overhead and enabling super-resolution estimation of channel parameters

while significantly lowering the computational complexity compared with existing methods.

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references

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