互耦效应下有源RIS辅助MIMO系统的信道估计算法

雷芳; 方洪武; 张靖涛

doi:10.11959/j.issn.1000-0801.DXKX260160

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互耦效应下有源RIS辅助MIMO系统的信道估计算法

更新时间：2026-07-03

- 互耦效应下有源RIS辅助MIMO系统的信道估计算法
- Mutual Coupling-Aware Channel Estimation for Active RIS-Assisted MIMO Systems
- 电信科学 2026年
- 作者机构：
  
  重庆邮电大学通信与信息工程学院重庆 400065
- 作者简介：
  
  雷芳（1972—），女，重庆邮电大学教授，主要研究方向为移动通信和电子新技术方面的应用。
  方洪武（2002—），男，重庆邮电大学通信与信息工程学院硕士生，主要研究方向为信道估计与可重构智能表面技术。
- 基金信息：
  
  重庆市自然科学基金创新发展联合基金（中国星网）(CSTB2023NSCQ-LZX0114)
- DOI：10.11959/j.issn.1000-0801.DXKX260160
  中图分类号： TN919.5
- 收稿：2026-03-10，
  
  修回：2026-05-14，
  
  录用：2026-06-23，
- 稿件说明：
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雷芳, 方洪武, 张靖涛. 互耦效应下有源RIS辅助MIMO系统的信道估计算法[J/OL]. 电信科学, 2026.

LEI Fang, FANG Hongwu, ZHANG Jingtao. Mutual Coupling-Aware Channel Estimation for Active RIS-Assisted MIMO Systems[J/OL]. Telecommunications Science, 2026.
雷芳, 方洪武, 张靖涛. 互耦效应下有源RIS辅助MIMO系统的信道估计算法[J/OL]. 电信科学, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260160.

LEI Fang, FANG Hongwu, ZHANG Jingtao. Mutual Coupling-Aware Channel Estimation for Active RIS-Assisted MIMO Systems[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260160.

摘要

针对有源可重构智能表面(Active Reconfigurable Intelligent Surface，Active RIS)辅助毫米波MIMO系统中互耦效应(Mutual Coupling，MC)引发的单元间电磁交互及其导致的反射矩阵非对角化问题，进而造成感知字典高相干、传统贪婪算法估计精度受限以及全维贝叶斯方法计算复杂度较高等挑战。本文首先构建了互耦效应的级联信道模型，并在此基础上提出两阶段稀疏贝叶斯学习算法。该方法首先基于互耦近似模型进行低复杂度候选支撑提取，然后利用字典缩减(Dictionary Reduction，DR)将问题投影至低维子空间，并在期望最大化(Expectation-Maximization，EM)框架下的SBL推断实现精细估计。仿真结果表明，所提方法在复杂度可控的前提下可有效提升互耦场景下的信道估计性能，较传统两阶段正交匹配追踪(Orthogonal Matching Pursuit，OMP)算法的归一化均方误差(Normalized Mean Squared Error，NMSE)平均降低约2dB。

Abstract

In active reconfigurable intelligent surface (Active RIS)-assisted millimeter-wave multiple-input multiple-output (MIMO) systems

mutual coupling (MC) induces electromagnetic interactions among RIS elements and renders the reflection matrix non-diagonal

which in turn leads to high dictionary coherence

limited estimation accuracy of conventional greedy algorithms

and high computational complexity of full-dimensional Bayesian methods. To address these issues

this paper establishes an MC-aware cascaded channel model and proposes a two-stage sparse Bayesian learning (SBL) channel estimation algorithm. In the proposed method

a low-complexity candidate support set is first obtained based on an approximate MC model

and dictionary reduction (DR) is then adopted to project the estimation problem into a low-dimensional subspace. Subsequently

refined channel estimation is achieved via SBL inference under the expectation-maximization (EM) framework. Simulation results verify that the proposed method significantly improves channel estimation performance in MC scenarios with controllable complexity

yielding an average normalized mean squared error (NMSE) gain of about 2 dB compared with the conventional two-stage orthogonal matching pursuit (OMP) algorithm.

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Keywords

references

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