Intelligent spectrum occupancy prediction for realistic measurements: GRU based approach

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dc.authorid0000-0001-9579-9342
dc.authorid0000-0001-9474-7372
dc.contributor.authorTusha, Armed
dc.contributor.authorKaplan, Batuhan
dc.contributor.authorÇırpan, Hakan Ali
dc.contributor.authorQaraqe, Khalid
dc.contributor.authorArslan, Hüseyin
dc.date.accessioned2022-09-30T11:08:03Z
dc.date.available2022-09-30T11:08:03Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü
dc.description.abstractCognitive radio (CR) technology has always been a research hotspot in the wireless communications field as it has the potential to significantly improve system capacity at the cost of increased processing time and power consumption, which represent highly critical performance indicators (CPI) towards next-generation wireless networks. In particular, the main problem in the CR-based communication links resides in the prediction of spectrum availability in accordance with strict secondary user (SU) CPIs requirements, which is not achievable through the traditional approaches. In this work, we design a novel hierarchical spectrum prediction model, taking advantage from the recurrent neural network (RNN) with the focus on the gated recurrent unit network (GRU). Specifically, the proposed system architecture offers an accrue prediction on the spectrum availability for the SU considering the prior information of the primary user (PU). The performance of the proposed design is illustrated through extensive simulation results. Specifically, real spectrum measurements gathered from Doha, in Qatar are performed to assess the performance accuracy of the designed architecture. In particular different from the conventional scheme that uses a binary representation of spectrum occupancy (idle is '0' and occupied is '1'), we perform training and prediction over the minimum and maximum recorded measurements.
dc.description.sponsorshipQatar National Research Funden_US
dc.identifier.citationTusha, A., Kaplan, B., Çırpan, H. A., Qaraqe, K. ve Arslan, H. (2022). Intelligent spectrum occupancy prediction for realistic measurements: GRU based approach. 2022 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom içinde (179-184. ss.). Sofia, 6-9 June 2022. https://dx.doi.org/10.1109/BlackSeaCom54372.2022.9858237
dc.identifier.doi10.1109/BlackSeaCom54372.2022.9858237
dc.identifier.endpage184
dc.identifier.isbn9781665497497
dc.identifier.scopus2-s2.0-85137938466
dc.identifier.scopusqualityN/A
dc.identifier.startpage179
dc.identifier.urihttps://dx.doi.org/10.1109/BlackSeaCom54372.2022.9858237
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9778
dc.identifier.wos000865848800031en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorTusha, Armed
dc.institutionauthorKaplan, Batuhan
dc.institutionauthorÇırpan, Hakan Ali
dc.institutionauthorArslan, Hüseyin
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2022 IEEE International Black Sea Conference on Communications and Networking, BlackSeaComen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectCognitive Radio
dc.subjectDeep Learning
dc.subjectMultidimensional Signal Analysis
dc.subjectSpectrum Measurement
dc.subjectSpectrum Occupancy
dc.titleIntelligent spectrum occupancy prediction for realistic measurements: GRU based approach
dc.typeArticle

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