dc.contributor.author | Aygül, Mehmet Ali | |
dc.contributor.author | Nazzal, Mahmoud | |
dc.contributor.author | Ekti, Ali Rıza | |
dc.contributor.author | Görçin, Ali | |
dc.contributor.author | da Costa, Daniel Benevides | |
dc.contributor.author | Ateş, Hasan Fehmi | |
dc.contributor.author | Arslan, Hüseyin | |
dc.date.accessioned | 2021-01-28T10:42:14Z | |
dc.date.available | 2021-01-28T10:42:14Z | |
dc.date.issued | 2020 | en_US |
dc.identifier.citation | Aygül, 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-Spring48590.2020.9129001 | en_US |
dc.identifier.isbn | 9781728152073 | |
dc.identifier.issn | 1550-2252 | |
dc.identifier.uri | https://dx.doi.org/10.1109/VTC2020-Spring48590.2020.9129001 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12511/6435 | |
dc.description.abstract | The 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.sponsorship | Qatar National Research Fund; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.rights | info:eu-repo/semantics/embargoedAccess | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Frequency Correlation | en_US |
dc.subject | Real-World Spectrum Measurement | en_US |
dc.subject | Spectrum Occupancy Prediction | en_US |
dc.title | Spectrum occupancy prediction exploiting time and frequency correlations through 2D-LSTM | en_US |
dc.type | conferenceObject | en_US |
dc.relation.ispartof | 91st IEEE Vehicular Technology Conference, VTC Spring | en_US |
dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | en_US |
dc.authorid | 0000-0002-1797-8238 | en_US |
dc.authorid | 0000-0003-3375-0310 | en_US |
dc.authorid | 0000-0002-6842-1528 | en_US |
dc.authorid | 0000-0001-9474-7372 | en_US |
dc.identifier.volume | 2020 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.identifier.doi | 10.1109/VTC2020-Spring48590.2020.9129001 | en_US |