Zhao, W, You, Y, Zhang, L orcid.org/0000-0002-4535-3200 et al. (2 more authors) (2023) OMPL-SBL Algorithm for Intelligent Reflecting Surface-Aided mmWave Channel Estimation. IEEE Transactions on Vehicular Technology. ISSN 0018-9545
Abstract
Channel estimation (CE) is critical for intelligent reflecting surface (IRS) aided millimeter wave (mmWave) multiple input multiple output (MIMO) systems. In this paper, we propose the orthogonal matching pursuit list-sparse Bayesian learning (OMPL-SBL) algorithm which divides the cascaded channel estimation into two stages. The first stage calculates the prior for sparse Bayesian learning (SBL) using orthogonal matching pursuit list (OMPL) exploiting the grid sparsity of the cascaded channel, and the second stage employs the prior to obtain the accurate estimation using SBL. The proposed algorithm is able to achieve high estimation accuracy with low computational complexity compared to \boldmath ℓ1 -minimization and Bayesian algorithms. In simulation, we show the proposed algorithm not only cuts down time complexity by more than 95% of the SBL algorithm, but also achieves a higher estimation accuracy.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2023 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. |
Keywords: | Sparse Bayesian learning (SBL), intelligent reflecting surface (IRS), channel estimation (CE), compressed sensing (CS) |
Dates: |
|
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: | 17 Mar 2023 15:02 |
Last Modified: | 21 Aug 2023 09:42 |
Status: | Published online |
Publisher: | IEEE |
Identification Number: | 10.1109/TVT.2023.3287400 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:196887 |