Mutual Coupling-Aware Channel Estimation for Active RIS-Assisted MIMO Systems

LEI Fang; FANG Hongwu; ZHANG Jingtao

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

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Mutual Coupling-Aware Channel Estimation for Active RIS-Assisted MIMO Systems

更新时间：2026-07-03

- Mutual Coupling-Aware Channel Estimation for Active RIS-Assisted MIMO Systems
- Telecommunications Science (2026)
- 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：
- 基金信息：
  
  Chongqing Natural Science Foundation Innovation and Development Joint Fund (SatNet)(CSTB2023NSCQ-LZX0114)
- DOI：10.11959/j.issn.1000-0801.DXKX260160
  CLC： TN919.5
- Received：10 March 2026，
  
  Revised：2026-05-14，
  
  Accepted：23 June 2026，
- 稿件说明：
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LEI Fang, FANG Hongwu, ZHANG Jingtao. Mutual Coupling-Aware Channel Estimation for Active RIS-Assisted MIMO Systems[J/OL]. Telecommunications Science, 2026.
DOI：

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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references

Basar E , Di Renzo M , De Rosny J , et al . Wireless communications through reconfigurable intelligent surfaces [J ] . IEEE access , 2019 , 7 : 116753 - 116773 .

Di Renzo M , Zappone A , Debbah M , et al . Smart radio environments empowered by reconfigurable intelligent surfaces: How it works, state of research, and the road ahead [J ] . IEEE journal on selected areas in communications , 2020 , 38 ( 11 ): 2450 - 2525 .

Zhang Z , Dai L , Chen X , et al . Active RIS vs. passive RIS: Which will prevail in 6G? [J ] . IEEE Transactions on Communications , 2022 , 71 ( 3 ): 1707 - 1725 .

Rao J , Zhang Y , Tang S , et al . An active reconfigurable intelligent surface utilizing phase-reconfigurable reflection amplifiers [J ] . IEEE Transactions on Microwave Theory and Techniques , 2023 , 71 ( 7 ): 3189 - 3202 .

Cao Y , Cheng W , Wang J , et al . Active reconfigurable intelligent surface assisted MIMO: Electromagnetic-compliant modeling with mutual coupling [J ] . IEEE Transactions on Wireless Communications , 2025 .

Nerini M , Shen S , Li H , et al . A universal framework for multiport network analysis of reconfigurable intelligent surfaces [J ] . IEEE Transactions on Wireless Communications , 2024 , 23 ( 10 ): 14575 - 14590 .

Li H , Shen S , Nerini M , et al . Beyond diagonal reconfigurable intelligent surfaces with mutual coupling: Modeling and optimization [J ] . IEEE Communications Letters , 2024 , 28 ( 4 ): 937 - 941 .

Zheng P , Wang R , Shamim A , et al . Mutual coupling in RIS-aided communication: Model training and experimental validation [J ] . IEEE Transactions on Wireless Communications , 2024 , 23 ( 11 ): 17174 - 17188 .

Zheng P , Ma X , Al-Naffouri T Y . On the impact of mutual coupling on RIS-assisted channel estimation [J ] . IEEE Wireless Communications Letters , 2024 , 13 ( 5 ): 1275 - 1279 .

Rabault A , Le Magoarou L , Sol J , et al . On the tacit linearity assumption in common cascaded models of RIS-parametrized wireless channels [J ] . IEEE Transactions on Wireless Communications , 2024 , 23 ( 8 ): 10001 - 10014 .

Chen J , Liang Y C , Cheng H V , et al . Channel estimation for reconfigurable intelligent surface aided multi-user mmWave MIMO systems [J ] . IEEE Transactions on Wireless Communications , 2023 , 22 ( 10 ): 6853 - 6869 .

Li K , He J , El-Hajjar M , et al . Localization in reconfigurable intelligent surface aided mmWave systems: A multiple measurement vector based channel estimation method [J ] . IEEE Transactions on Vehicular Technology , 2024 , 73 ( 9 ): 13326 - 13339 .

Yang H , Zhang A , Sun Y , et al . Regularized Sparse Bayesian Learning Based Channel Estimation for RIS-Assisted Wireless Communication System [J ] . IEEE Communications Letters , 2024 , 28 ( 6 ): 1412 - 1416 .

Yang H , Zhang A , Tian D , et al . Dual-Structure Regularized Sparse Bayesian Learning Channel Estimation for RIS Assisted Wireless Communication [C ] // 2023 9th International Conference on Computer and Communications (ICCC) . IEEE , 2023 : 924 - 929 .

Bayraktar M , González-Prelcic N , Alexandropoulos G C , et al . RIS-aided joint channel estimation and localization at mmWave under hardware impairments: A dictionary learning-based approach [J ] . IEEE Transactions on Wireless Communications , 2024 , 23 ( 12 ): 19696 - 19712 .

Destino G , Juntti M , Nagaraj S . Leveraging sparsity into massive MIMO channel estimation with the adaptive-LASSO [C ] // 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP) . IEEE , 2015 : 166 - 170 .

Fan J , Dou X , Zou W , et al . Localization based on improved sparse Bayesian learning in mmWave MIMO systems [J ] . IEEE Transactions on Vehicular Technology , 2022 , 71 ( 1 ): 354 - 361 .

Tropp J A , Gilbert A C . Signal recovery from random measurements via orthogonal matching pursuit [J ] . IEEE Transactions on information theory , 2007 , 53 ( 12 ): 4655 - 4666 .

Wipf D P , Rao B D . Sparse Bayesian learning for basis selection [J ] . IEEE Transactions on Signal processing , 2004 , 52 ( 8 ): 2153 - 2164 .

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