Deep learning-based channel estimation for OFDM-IM systems over Rayleigh fading channels

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dc.authoridAdiguzel, Omer/0000-0003-3683-3082
dc.contributor.authorAdiguzel, Omer
dc.contributor.authorDeveli, Ibrahim
dc.date.accessioned2025-03-17T12:27:46Z
dc.date.available2025-03-17T12:27:46Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description.abstractDeep learning (DL)-based channel estimation for orthogonal frequency division multiplexing with index modulation (OFDM-IM) under Rayleigh fading channel conditions is presented in this paper. A deep neural network (DNN) is utilized to estimate the channel response in simulations. The proposed DNN is trained using the channel coefficient derived through the least squares (LS) method. Then channel estimation is conducted using the trained DNN. Within the DNN, the long short-term memory (LSTM) layer is included as the hidden layer. Different scenarios are handled in simulations and the proposed DNN is compared with traditional channel estimation methods. The simulations demonstrate that the DL-based channel estimation significantly surpasses the LS and minimum mean-square error (MMSE) techniques. This paper presents a deep learning (DL)-based channel estimation method for orthogonal frequency division multiplexing with index modulation (OFDM-IM) under Rayleigh fading channel conditions. The proposed method employs a Long Short-Term Memory (LSTM)-based network for channel estimation. The results demonstrate that the DL-based channel estimation significantly outperforms the least squares (LS) and minimum mean-square error (MMSE) techniques. image
dc.description.sponsorshipScientific and Technological Research Council of Turkey [122R052]; Erciyes University Scientific Research Projects Coordination Unit [FDK-2022-11947]
dc.description.sponsorshipScientific and Technological Research Council of Turkey, Grant/Award Number:122R052; Erciyes University Scientific Research Projects Coordination Unit,Grant/Award Number: FDK-2022-11947
dc.identifier.doi10.1002/dac.5944
dc.identifier.issn1074-5351
dc.identifier.issn1099-1131
dc.identifier.issue18
dc.identifier.scopus2-s2.0-85200576291
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1002/dac.5944
dc.identifier.urihttps://hdl.handle.net/20.500.13099/2415
dc.identifier.volume37
dc.identifier.wosWOS:001285492700001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofInternational Journal of Communication Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250316
dc.subjectchannel estimation
dc.subjectdeep learning
dc.subjectindex modulation
dc.subjectleast squares (LS)
dc.subjectminimum mean-square error (MMSE)
dc.subjectOFDM-IM
dc.titleDeep learning-based channel estimation for OFDM-IM systems over Rayleigh fading channels
dc.typeArticle

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