Exploiting sparsity recovery for compressive spectrum sensing: A machine learning approach

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dc.authorid0000-0003-3375-0310
dc.authorid0000-0001-9474-7372
dc.contributor.authorNazzal, Mahmoud
dc.contributor.authorEkti, Ali Rıza
dc.contributor.authorGörçin, Ali
dc.contributor.authorArslan, Hüseyin
dc.date.accessioned2019-12-26T07:55:16Z
dc.date.available2019-12-26T07:55:16Z
dc.date.issued2019
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.descriptionThis work was supported by the Scientific and Research Council of Turkey (TUBITAK) = Bu çalışma TÜBİTAK tarafından desteklenmiştir.
dc.description.abstractSub-Nyquist sampling for spectrum sensing has the advantages of reducing the sampling and computational complexity burdens. However, determining the sparsity of the underlying spectrum is still a challenging issue for this approach. Along this line, this paper proposes an algorithm for narrowband spectrum sensing based on tracking the convergence patterns in sparse coding of compressed received signals. First, a compressed version of a received signal at the location of interest is obtained according to the principle of compressive sensing. Then, the signal is reconstructed via sparse recovery over a learned dictionary. While performing sparse recovery, we calculate the sparse coding convergence rate in terms of the decay rate of the energy of residual vectors. Such a decay rate is conveniently quantified in terms of the gradient operator. This means that while compressive sensing allows for sub-Nyquist sampling thereby reducing the analog-to-digital conversion overhead, the sparse recovery process could be effectively exploited to reveal spectrum occupancy. Furthermore, as an extension to this approach, we consider feeding the energy decay gradient vectors as features for a machine learning-based classification process. This classification further enhances the performance of the proposed algorithm. The proposed algorithm is shown to have excellent performances in terms of the probability-of-detection and false-alarm-rate measures. This result is validated through numerical experiments conducted over synthetic data as well as real-life measurements of received signals. Moreover, we show that the proposed algorithm has a tractable computational complexity, allowing for real-time operation.
dc.identifier.citationNazzal, M., Ekti, A. R., Görçin, A., Arslan, H. (2019). Exploiting sparsity recovery for compressive spectrum sensing: A machine learning approach. IEEE Access, 7, 126098-126110. http://doi.org/10.1109/ACCESS.2019.2909976.
dc.identifier.doi10.1109/ACCESS.2019.2909976
dc.identifier.endpage126110
dc.identifier.issn2169-3536
dc.identifier.scopusqualityQ1
dc.identifier.startpage126098
dc.identifier.urihttp://doi.org/10.1109/ACCESS.2019.2909976
dc.identifier.urihttps://hdl.handle.net/20.500.12511/4716
dc.identifier.volume7
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectMachine Learning Classification
dc.subjectResidual Components
dc.subjectSparse Coding
dc.subjectSpectrum Sensing
dc.titleExploiting sparsity recovery for compressive spectrum sensing: A machine learning approach
dc.typeArticle

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