Estimating multi-dimensional sparsity level for spectrum sensing

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dc.authorid0000-0003-3375-0310
dc.authorid0000-0001-9474-7372
dc.contributor.authorAygül, Mehmet Ali
dc.contributor.authorNazzal, Mahmoud
dc.contributor.authorArslan, Hüseyin
dc.date.accessioned2023-06-02T11:45:57Z
dc.date.available2023-06-02T11:45:57Z
dc.date.issued2023
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü
dc.description.abstractIdentifying spectrum opportunities is a crucial element of efficient spectrum utilization for future wireless networks. Spectrum sensing offers a convenient means for revealing such opportunities. Studies showed that usage of the spectrum has a high correlation over multi-dimensions, including time and frequency. However, multi-dimensional spectrum sensing requires high-cost processes. Applying compressive sensing allows for subNyquist sampling. This reduces associated training, feedback, and computation overheads of a spectrum sensing method. However, the accuracy of the signal sparsity assumption and knowledge of the precise sparsity level are necessary for the applicability of compressive sensing. It is common practice to assume a level of known sparsity. On the other hand, in reality, this presumption is incorrect. This paper proposes a method for estimating the multidimensional sparsity for spectrum sensing. By extrapolating it from its counterpart with respect to a compact discrete Fourier basis, the proposed method calculates the sparsity level over a dictionary. A machine learning estimation method achieves this inference. Extensive simulations validate a high-quality sparsity estimation. To validate this observation, real-world measurements are used, where one of the biggest Turkish telecom operators has private uplink bands in the frequency range between 852-856 MHz.
dc.identifier.citationAygül, M. A., Nazzal, M. ve Arslan, H. (2023). Estimating multi-dimensional sparsity level for spectrum sensing. IEEE Wireless Communications and Networking Conference (WCNC). Glasgow, Scotland, 26-29 March 2023. https://doi.org/10.1109/WCNC55385.2023.10118987
dc.identifier.doi10.1109/WCNC55385.2023.10118987
dc.identifier.isbn9781665491228
dc.identifier.issn1525-3511
dc.identifier.scopus2-s2.0-85159781568
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/WCNC55385.2023.10118987
dc.identifier.urihttps://hdl.handle.net/20.500.12511/11021
dc.identifier.volume2023
dc.identifier.wos000989491900328en_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 (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.subjectCompressive Sensing
dc.subjectMachine Learning
dc.subjectRealworld Measurements
dc.subjectSparsity Level Estimation
dc.subjectSpectrum Sensing
dc.titleEstimating multi-dimensional sparsity level for spectrum sensing
dc.typeConference Object

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