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dc.contributor.authorAygül, Mehmet Ali
dc.contributor.authorNazzal, Mahmoud
dc.contributor.authorEkti, Ali Rıza
dc.contributor.authorGörçin, Ali
dc.contributor.authorda Costa, Daniel Benevides
dc.contributor.authorAteş, Hasan Fehmi
dc.contributor.authorArslan, Hüseyin
dc.date.accessioned2021-01-28T10:42:14Z
dc.date.available2021-01-28T10:42:14Z
dc.date.issued2020en_US
dc.identifier.citationAygül, M. A., Nazzal, M., Ekti, A. R., Görçin, A., da Costa, D. B., Ateş, H. F. ... Arslan, H. (2020). Spectrum occupancy prediction exploiting time and frequency correlations through 2D-LSTM. 91st IEEE Vehicular Technology Conference, VTC Spring. Antwerp, Belgium, 25-28 May 2020. https://dx.doi.org/10.1109/VTC2020-Spring48590.2020.9129001en_US
dc.identifier.isbn9781728152073
dc.identifier.issn1550-2252
dc.identifier.urihttps://dx.doi.org/10.1109/VTC2020-Spring48590.2020.9129001
dc.identifier.urihttps://hdl.handle.net/20.500.12511/6435
dc.description.abstractThe identification of spectrum opportunities is a pivotal requirement for efficient spectrum utilization in cognitive radio systems. Spectrum prediction offers a convenient means for revealing such opportunities based on the previously obtained occupancies. As spectrum occupancy states are correlated over time, spectrum prediction is often cast as a predictable time-series process using classical or deep learning-based models. However, this variety of methods exploits time-domain correlation and overlooks the existing correlation over frequency. In this paper, differently from previous works, we investigate a more realistic scenario by exploiting correlation over time and frequency through a 2D-long short-term memory (LSTM) model. Extensive experimental results show a performance improvement over conventional spectrum prediction methods in terms of accuracy and computational complexity. These observations are validated over the real-world spectrum measurements, assuming a frequency range between 832-862 MHz where most of the telecom operators in Turkey have private uplink bands.en_US
dc.description.sponsorshipQatar National Research Fund; Türkiye Bilimsel ve Teknolojik Araştirma Kurumuen_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectDeep Learningen_US
dc.subjectFrequency Correlationen_US
dc.subjectReal-World Spectrum Measurementen_US
dc.subjectSpectrum Occupancy Predictionen_US
dc.titleSpectrum occupancy prediction exploiting time and frequency correlations through 2D-LSTMen_US
dc.typeconferenceObjecten_US
dc.relation.ispartof91st IEEE Vehicular Technology Conference, VTC Springen_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_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.volume2020en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1109/VTC2020-Spring48590.2020.9129001en_US


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