Channel Estimation in RIS-Assisted MIMO Systems Operating Under Imperfections

Gomes, Paulo R. B.; Araújo, Gilderlan T. de; Sokal, Bruno; Almeida, André L. F. de; Makki, Behrooz; Fodor, Gábor

doi:10.1109/tvt.2023.3279805

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…Thus, it is desirable to be able to recover the UT-RIS and RIS-BS channels individually. More recently, tensor-based approaches have been applied to the area of RIS-aided wireless communications and have been proven to enable the decoupling of the BS-RIS and RIS-UT channels as well as providing more flexible system parameter settings to be used for training [21,22]. These two works capitalize on the tensor CP decomposition by developing costeffective iterative algorithms based on the ALS frame to solve the CE problem.…”

Section: Computational Complexity As Explored Inmentioning

confidence: 99%

“…We summarized this correlationbased scheme as Algorithm 2. The scaling matrices {𝐃 1 , 𝐃 2 , 𝐃 3 } affecting the columns of the estimated matrices can be eliminated by simple normalization procedure, as shown in [22] and [35,36]. Now we discuss how to estimate the channel parameters from 𝐇[𝑞] based on {𝐗 ̂, 𝐂 ̂}.…”

Section: Channel Parameters Estimationmentioning

confidence: 99%

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Slow-Moving Channel Estimation via Vandermonde Structured Tensor Decomposition in RIS-Aided MIMO Systems

He,

Harms,

Yang

2024

IEEE Access

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This paper studies the problem of slow-moving channel estimation (CE) in the physical layer of a reconfigurable intelligent surface (RIS)-aided multiple-input multiple-output (MIMO) communications system. Acquiring accurate channel state information (CSI) in such a system is one of the critical challenges due to a large number of RIS elements, especially under mobility with its characteristic Doppler effect. In this paper, we propose a channel estimation framework based on the tensor decomposition method to decouple the conventional cascaded channel. We formulate the uplink training signals as a third-order tensor, which admits a CANDECOMP/PARAFAC (CP) model with three factor matrices containing the CSI and phase information of the RIS. We further utilize the third factor matrix featured in the Vandermonde structure and decompose the received tensor signals with a higher rank into three factor matrices. Additionally, we develop algorithms to estimate parameters of the low-velocity user terminal (UT) -RIS channel from the resultant factor matrices. The numerical results indicate that our proposed tensor-based algorithms allow the estimation of individual UT -RIS and RIS -base station (BS) channels with expected accuracy and strong robustness.

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Section: Computational Complexity As Explored Inmentioning

confidence: 99%

Section: Channel Parameters Estimationmentioning

confidence: 99%

Slow-Moving Channel Estimation via Vandermonde Structured Tensor Decomposition in RIS-Aided MIMO Systems

He,

Harms,

Yang

2024

IEEE Access

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“…Then, [9] proposes a tensor-based receiver formulated as a semi-blind problem that jointly estimates the involved channels and transmitted data. The work in [10] proposes a set of two tensor-based algorithms to do channel parameter estimation under unknown IRS hardware impairments. In our previous work [11], we propose a two-stage tensor-based framework for parametric channel parameter estimation and data detection of time-varying channels based on a 4th order PARAFAC model.…”

Section: Introductionmentioning

confidence: 99%

Channel Estimation and Joint Beamforming Design for Multi-IRS MIMO systems

A.¹,

Sokal²,

Almeida³

2021

Anais Do XXXIX Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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In this paper, we propose a channel estimation and joint beamforming design method for multi-IRS multiple-input and multiple-output (MIMO) systems. We investigate the scenario where multiple intelligent reflecting surfaces (IRSs) are located on the exact spot of the channel scatterers. We investigate the spectral efficiency (SE), benefits and drawbacks of deploying multiple IRSs in comparison with a scenario with a single or no IRS. Our simulation results show a trade-off between the number of reflecting elements and the total number of IRSs in the system.

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