Deep Learning Aided Channel Estimation in Intelligent Reflecting Surfaces

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dc.authoridMUTLU, URAL/0000-0003-2595-0531
dc.contributor.authorMutlu, Ural
dc.contributor.authorKabalci, Yasin
dc.date.accessioned2024-11-07T13:23:58Z
dc.date.available2024-11-07T13:23:58Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description5th IEEE Global Power, Energy and Communication Conference (GPECOM) -- JUN 14-16, 2023 -- Nevsehir, TURKEY
dc.description.abstractIntelligent Reflecting Surfaces (IRS) consist of multiple independently controllable passive reflecting elements that can change the phase and the amplitude of the reflected signals to achieve passive beamforming. To facilitate passive beamforming, channel state information (CSI) needs to be available at the Base Station so that the optimum reflection pattern can be calculated. Therefore, the objective of this research is to present a Deep Learning (DL) based approach to improve the accuracy of the channel estimation in an IRS aided Multiple Input Single Output - Orthogonal Frequency Division Multiplexing (MISO-OFDM) wireless network. A Convolutional Neural Network (CNN) that treats OFDM frames as images is adapted for IRS and applied to the direct and cascaded channels. The CNN presented in the study is trained with channel coefficients obtained by Least Squares (LS) method and Discrete Fourier Transform (DFT) as the reflection pattern at the IRS. The results show that the CNN improves channel estimation efficiency by reducing the effects of noise and improving the Normalized Mean Square Error (NMSE) parameters.
dc.description.sponsorshipIEEE,Nevsehir Haci Bektas Veli Univ,IEEE Ind Applicat Soc,IEEE Ind Elect Soc,IEEE Power Elect Soc,IEEE Power & Energy Soc,Univ Nova Lisboa,Univ Palermo
dc.identifier.doi10.1109/GPECOM58364.2023.10175750
dc.identifier.endpage518
dc.identifier.isbn979-8-3503-0198-4
dc.identifier.issn2832-7667
dc.identifier.scopus2-s2.0-85166468462
dc.identifier.scopusqualityN/A
dc.identifier.startpage513
dc.identifier.urihttps://doi.org/10.1109/GPECOM58364.2023.10175750
dc.identifier.urihttps://hdl.handle.net/11480/13843
dc.identifier.wosWOS:001043011400087
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2023 5th Global Power, Energy and Communication Conference, Gpecom
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectIntelligent Reflecting Surfaces
dc.subjectChannel Estimation
dc.subjectDeep Learning
dc.subjectConvolutional Neural Network.
dc.titleDeep Learning Aided Channel Estimation in Intelligent Reflecting Surfaces
dc.typeConference Object

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