You, Y, Zhang, L orcid.org/0000-0002-4535-3200, Yang, M et al. (3 more authors) (2022) Structured OMP for IRS-aided mmWave Channel Estimation by Exploiting Angular Spread. IEEE Transactions on Vehicular Technology, 71 (4). pp. 4444-4448. ISSN 0018-9545
Abstract
An emerging technology namely the intelligent reflecting surface (IRS) can be deployed in millimeter wave (mmWave) communication to overcome the large propagation loss and huge power consumption induced by the short wavelength. However, the large number of passive IRS elements without signal processing abilities induces high pilot overhead for channel estimation (CE). In this paper, the angular spread feature or also referred to the cluster feature is exploited to formulate the CE problem as a structured sparse signal recovery problem. Then, structured orthogonal matching pursuit (S-OMP) algorithm is proposed to efficiently solve the problem by utilizing the structure of the channel matrix in row and column simultaneously. Simulation results demonstrate that S-OMP reduces more than 40% pilot overhead while maintaining the same performance compared with the existing methods.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Jan 2022 13:35 |
Last Modified: | 26 Jul 2022 14:14 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TVT.2022.3142285 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:182180 |