Expectation maximization—vector approximate message passing based generalized linear model for channel estimation in intelligent reflecting surface-assisted millimeter multi-user multiple-input multiple-output systems

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dc.authorscopusidCengiz Korhan / 56522820200
dc.authorwosidCengiz Korhan / ABD-5559-2020
dc.contributor.authorShoukath, Ali K.
dc.contributor.authorSajan, Philip P.
dc.contributor.authorKhan, Arfat Ahmad
dc.contributor.authorMoses, Leeban
dc.contributor.authorCengiz, Korhan
dc.contributor.authorAkleylek, Sedat
dc.date.accessioned2025-04-18T09:59:13Z
dc.date.available2025-04-18T09:59:13Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractChannel estimation poses a main challenge in intelligent reflecting surface (IRS)assisted millimeter wave (mmWave) multi-user multiple-input multiple-output (MIMO) systems due to the substantial number of antennas at the base station (BS) and the passive reflective elements within the IRS lacking sufficient signal processing capabilities. This article addresses this challenge by proposing a channel estimation technique for IRS-assisted mmWave MIMO systems. The problem of channel estimation is normally taken as a compressed sensing (CS) problem, typically addressed through algorithms such as Orthogonal Matching Pursuit (OMP), Generalized Approximate Message Passing (GAMP), and Vector Approximate Message Passing with Expectation-Maximization (EM-VAMP). EM-VAMP demonstrates better performance only when a Gaussian mixture (GM) distribution is chosen as the prior for the sparse channel, especially at high signal-to-noise ratios (SNRs). To address this, the article introduces the application of generalized linear models (GLMs), extensions of standard linear models, providing increased flexibility in modeling data that deviates from Gaussian distribution. Numerical results unveil that the proposed Its EM-VAMP-GLM is much more robust to the existing OMP, GAMP and EM-LAMP algorithms. Copyright 2025 K et al. Distributed under Creative Commons CC-BY 4.0
dc.identifier.citationPhilip, S. P., Khan, A. A., Moses, L., Cengiz, K., Akleylek, S., & Ivković, N. (2025). Expectation maximization—vector approximate message passing based generalized linear model for channel estimation in intelligent reflecting surface-assisted millimeter multi-user multiple-input multiple-output systems. PeerJ Computer Science, 11, e2582.
dc.identifier.doi10.7717/PEERJ-CS.2582
dc.identifier.issn23765992
dc.identifier.scopus2-s2.0-85216310392
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.7717/PEERJ-CS.2582
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6907
dc.identifier.volume11
dc.indekslendigikaynakScopus
dc.institutionauthorCengiz, Korhan
dc.institutionauthoridCengiz Korhan / 0000-0001-6594-8861
dc.language.isoen
dc.publisherPeerJ Inc.
dc.relation.ispartofPeerJ Computer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectApproximate Message Passing
dc.subjectIntelligent Reflecting Surface
dc.subjectMillimeter Wave Communication
dc.subjectShrinkage Function
dc.titleExpectation maximization—vector approximate message passing based generalized linear model for channel estimation in intelligent reflecting surface-assisted millimeter multi-user multiple-input multiple-output systems
dc.typeArticle

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