Estimation and exploitation of multidimensional sparsity for MIMO-OFDM channel estimation

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dc.authorid0000-0003-3375-0310
dc.authorid0000-0001-9474-7372
dc.contributor.authorNazzal, Mahmoud
dc.contributor.authorAygül, Mehmet Ali
dc.contributor.authorArslan, Hüseyin
dc.date.accessioned2022-06-03T12:20:37Z
dc.date.available2022-06-03T12:20:37Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü
dc.description.abstractObtaining accurate channel state estimates at reasonable training overheads remains a big challenge for the applicability of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM). Recently, the exploitation of channel sparsity has led to sub-Nyquist channel sampling thereby reducing the channel training overhead. Still, there is a growing belief in channel sparsity appearance in many dimensions; time, frequency, angle, and space. Accordingly, this paper proposes an algorithm for channel estimation where sparsity in multidimensions is simultaneously exploited. Also, the applicability of sparse coding relies on the validity of a signal sparsity assumption and knowing the exact sparsity level. However, this assumption is not valid in practice, especially when applying learned dictionaries as sparsifying transforms. The problem is more strongly pronounced with multidimensional sparsity. In this paper, we also propose an algorithm for estimating the composite sparsity lying in multiple domains defined by learned dictionaries. Simulations validate a substantial channel estimation quality attained by the proposed algorithm as compared to the existing algorithms. The simulations also validate a high quality of sparsity estimation leading to performances close to the impractical case of assuming known sparsity.
dc.identifier.citationNazzal, M., Aygül, M. A. ve Arslan, H. (2022). Estimation and exploitation of multidimensional sparsity for MIMO-OFDM channel estimation. IEEE Wireless Communications and Networking Conference (IEEE WCNC) içinde (980-985. ss.). Austin, 10-13 April 2022. https://dx.doi.org/10.1109/WCNC51071.2022.9771576
dc.identifier.doi10.1109/WCNC51071.2022.9771576
dc.identifier.endpage985
dc.identifier.isbn9781665442664
dc.identifier.issn1525-3511
dc.identifier.scopus2-s2.0-85130711351
dc.identifier.scopusqualityN/A
dc.identifier.startpage980
dc.identifier.urihttps://dx.doi.org/10.1109/WCNC51071.2022.9771576
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9498
dc.identifier.wos000819473100166en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorNazzal, Mahmoud
dc.institutionauthorArslan, Hüseyin
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Wireless Communications and Networking Conference (IEEE WCNC)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.relation.tubitakinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/119E433
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectChannel Estimation
dc.subjectMassive MIMO
dc.subjectMultidimensional Sparsity
dc.subjectOFDM
dc.subjectSparsity Level Estimation
dc.titleEstimation and exploitation of multidimensional sparsity for MIMO-OFDM channel estimation
dc.typeConference Object

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