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dc.contributor.authorBüyükşar, Ayşe Betül
dc.contributor.authorŞenol, Habib
dc.contributor.authorErküçük, Serhat
dc.contributor.authorCirpan, Hakan Ali
dc.date.accessioned2019-06-27T08:02:28Z
dc.date.available2019-06-27T08:02:28Z
dc.date.issued2019
dc.identifier.isbn978-981-13-0408-8
dc.identifier.isbn978-981-13-0407-1
dc.identifier.issn1876-1100
dc.identifier.issn1876-1119
dc.identifier.urihttps://hdl.handle.net/20.500.12469/626
dc.identifier.urihttps://doi.org/10.1007/978-981-13-0408-8_25
dc.description.abstractIt is expected from future communication standards that channel estimation algorithms should be able to operate over very fast varying frequency selective channel models. Therefore in this study autoregressive (AR) modeled fast varying channel has been considered and tracked with Kalman filter over one orthogonal frequency division multiplexing (OFDM) symbol. Channel sparsity is exploited which decreases the complexity requirements of the Kalman algorithm. Since Kalman filter is not directly applicable to sparse channels orthogonal matching pursuit (OMP) algorithm is modified for AR modeled sparse signal estimation. Also by using windows sparsity detection errors have been decreased. The simulation results showed that sparse fast varying channel can be tracked with the proposed hybrid Kalman-OMP algorithm and windowing method offers improved MSE results.
dc.language.isoEnglish
dc.publisherSpringer
dc.subjectOFDM
dc.subjectFast time-varying channel
dc.subjectAutoregressive model
dc.subjectKalman
dc.subjectOMP
dc.subjectSparse channel tracking
dc.titleRapidly Varying Sparse Channel Tracking with Hybrid Kalman-OMP Algorithm
dc.typeProceedings Paper
dc.identifier.startpage289
dc.identifier.endpage298
dc.relation.journalInternational Telecommunications Conference
dc.identifier.volume504
dc.identifier.wosWOS:000454345100025
dc.identifier.doi10.1007/978-981-13-0408-8_25
dc.contributor.khasauthorŞenol, Habib
dc.contributor.khasauthorErküçük, Serhat


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