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dc.contributor.authorAygül, Mehmet Ali
dc.contributor.authorNazzal, Mahmoud
dc.contributor.authorSağlam, Mehmet İzzet
dc.contributor.authorda Costa, Daniel Benevides
dc.contributor.authorAteş, Hasan Fehmi
dc.contributor.authorArslan, Hüseyin
dc.date.accessioned2021-01-28T06:43:34Z
dc.date.available2021-01-28T06:43:34Z
dc.date.issued2021en_US
dc.identifier.citationAygül, M. A., Nazzal, M., Sağlam, M. İ., da Costa, D. B., Ateş, H. F. ve Arslan, H. (2021). Efficient spectrum occupancy prediction exploiting multidimensional correlations through composite 2D-LSTM models. Sensors, 21(1). https://dx.doi.org/10.3390/s21010135en_US
dc.identifier.issn1424-8220
dc.identifier.urihttps://dx.doi.org/10.3390/s21010135
dc.identifier.urihttps://hdl.handle.net/20.500.12511/6425
dc.description.abstractIn cognitive radio systems, identifying spectrum opportunities is fundamental to efficiently use the spectrum. Spectrum occupancy prediction is a convenient way of revealing opportunities based on previous occupancies. Studies have demonstrated that usage of the spectrum has a high correlation over multidimensions, which includes time, frequency, and space. Accordingly, recent literature uses tensor-based methods to exploit the multidimensional spectrum correlation. However, these methods share two main drawbacks. First, they are computationally complex. Second, they need to re-train the overall model when no information is received from any base station for any reason. Different than the existing works, this paper proposes a method for dividing the multidimensional correlation exploitation problem into a set of smaller sub-problems. This division is achieved through composite two-dimensional (2D)-long short-term memory (LSTM) models. Extensive experimental results reveal a high detection performance with more robustness and less complexity attained by the proposed method. The real-world measurements provided by one of the leading mobile network operators in Turkey validate these results.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectCognitive Radioen_US
dc.subjectDeep Learningen_US
dc.subjectMultidimensionsen_US
dc.subjectReal-World Spectrum Measurementen_US
dc.subjectSpectrum Occupancy Predictionen_US
dc.titleEfficient spectrum occupancy prediction exploiting multidimensional correlations through composite 2D-LSTM modelsen_US
dc.typearticleen_US
dc.relation.ispartofSensorsen_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümüen_US
dc.authorid0000-0002-1797-8238en_US
dc.authorid0000-0003-3375-0310en_US
dc.authorid0000-0002-6842-1528en_US
dc.authorid0000-0001-9474-7372en_US
dc.identifier.volume21en_US
dc.identifier.issue1en_US
dc.relation.ecinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/5200030
dc.relation.ecinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/3171084
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.3390/s21010135en_US
dc.identifier.wosqualityQ1en_US
dc.identifier.scopusqualityQ1en_US


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