dc.contributor.author | Aygül, Mehmet Ali | |
dc.contributor.author | Nazzal, Mahmoud | |
dc.contributor.author | Sağlam, Mehmet İzzet | |
dc.contributor.author | da Costa, Daniel Benevides | |
dc.contributor.author | Ateş, Hasan Fehmi | |
dc.contributor.author | Arslan, Hüseyin | |
dc.date.accessioned | 2021-01-28T06:43:34Z | |
dc.date.available | 2021-01-28T06:43:34Z | |
dc.date.issued | 2021 | en_US |
dc.identifier.citation | Aygül, 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/s21010135 | en_US |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | https://dx.doi.org/10.3390/s21010135 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12511/6425 | |
dc.description.abstract | In 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.iso | eng | en_US |
dc.publisher | MDPI | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Cognitive Radio | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Multidimensions | en_US |
dc.subject | Real-World Spectrum Measurement | en_US |
dc.subject | Spectrum Occupancy Prediction | en_US |
dc.title | Efficient spectrum occupancy prediction exploiting multidimensional correlations through composite 2D-LSTM models | en_US |
dc.type | article | en_US |
dc.relation.ispartof | Sensors | 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 | 21 | en_US |
dc.identifier.issue | 1 | en_US |
dc.relation.ec | info:eu-repo/grantAgreement/TUBITAK/SOBAG/5200030 | |
dc.relation.ec | info:eu-repo/grantAgreement/TUBITAK/SOBAG/3171084 | |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.doi | 10.3390/s21010135 | en_US |
dc.identifier.wosquality | Q1 | en_US |
dc.identifier.scopusquality | Q1 | en_US |