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
Channel 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 s...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2582.pdf |
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| author | Shoukath Ali K Sajan P Philip Arfat Ahmad Khan Leeban Moses Korhan Cengiz Sedat Akleylek Nikola Ivković |
| author_facet | Shoukath Ali K Sajan P Philip Arfat Ahmad Khan Leeban Moses Korhan Cengiz Sedat Akleylek Nikola Ivković |
| author_sort | Shoukath Ali K |
| collection | DOAJ |
| description | Channel 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. |
| format | Article |
| id | doaj-art-45438b8d520141bc95e842410f1b7323 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-45438b8d520141bc95e842410f1b73232025-01-05T15:05:06ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e258210.7717/peerj-cs.2582Expectation maximization—vector approximate message passing based generalized linear model for channel estimation in intelligent reflecting surface-assisted millimeter multi-user multiple-input multiple-output systemsShoukath Ali K0Sajan P Philip1Arfat Ahmad Khan2Leeban Moses3Korhan Cengiz4Sedat Akleylek5Nikola Ivković6Department of Electronics and Communication Engineering, Presidency University, Bengaluru, Karnataka, IndiaDepartment of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Erode, Tamil Nadu, IndiaDepartment of Computer Science, College of Computing, Khon Kaen University, Khon Kaen, ThailandDepartment of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Erode, Tamil Nadu, IndiaDepartment of Electrical-Electronics Engineering, Istinye University, Istanbul, TurkeyInstitute of Computer Science, University of Tartu, Tartu, EstoniaFaculty of Organization and Informatics, University of Zagreb, Pavlinska, Varaždin, CroatiaChannel 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.https://peerj.com/articles/cs-2582.pdfMillimeter wave communicationApproximate message passingIntelligent reflecting surfaceShrinkage function |
| spellingShingle | Shoukath Ali K Sajan P Philip Arfat Ahmad Khan Leeban Moses Korhan Cengiz Sedat Akleylek Nikola Ivković 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 Millimeter wave communication Approximate message passing Intelligent reflecting surface Shrinkage function |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Millimeter wave communication Approximate message passing Intelligent reflecting surface Shrinkage function |
| url | https://peerj.com/articles/cs-2582.pdf |
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