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dc.contributor.authorNazzal, Mahmoud
dc.contributor.authorHasekioǧlu, Orkun
dc.contributor.authorEkti, Ali Rıza
dc.contributor.authorGörçin, Ali
dc.contributor.authorArslan, Hüseyin
dc.date.accessioned2020-01-02T07:37:33Z
dc.date.available2020-01-02T07:37:33Z
dc.date.issued2019en_US
dc.identifier.citationNazzal, M., Hasekioǧlu, O., Ekti, A. R., Görçin, A. ve Arslan, H. (2019). Compressed spectrum sensing using sparse recovery convergence patterns through machine learning classification. 30th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). Istanbul, Turkey, 8-11 September 2019. IEEE. http://doi.org/10.1109/PIMRC.2019.8904321en_US
dc.identifier.isbn9781538681107
dc.identifier.urihttp://doi.org/10.1109/PIMRC.2019.8904321
dc.identifier.urihttps://hdl.handle.net/20.500.12511/4849
dc.description.abstractDespite the well-known success of sub-Nyquist sampling in reducing the hardware and computational costs of spectrum sensing, it still has the shortcoming of requiring a pre-determined spectrum sparsity level. This paper proposes an algorithm for sub-Nyquist wide-band spectrum sensing addressing this shortcoming. The proposed algorithm divides the spectrum into narrow, contagious frequency subbands and learns a subband dictionary for each subband. A subband dictionary is well-suited for the representation of signals in its corresponding subband. A compressed version of the received signal is sparsely coded over each subband dictionary. We show that the convergence patterns over a specific dictionary can be used for identifying the occupancy of its underlying subband. Therefore, the convergence patterns obtained by the gradient operator are used as distinctive classifying features. Then, a machine learning-based classifier is trained over these features and used to make the decision about spectrum occupancy. As the interest is only to characterize sparse coding convergence patterns, we alleviate the need for a specific or an estimated sparsity level. Besides, using subband dictionaries at different frequencies omits the need for a frequency-splitting filterbank. The proposed algorithm achieves significant performance improvements in terms of the probability-of-detection and false-alarm-rate measures. This result is validated through simulations with various operating scenarios.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectMachine Learningen_US
dc.subjectCompressed Spectrum Sensingen_US
dc.subjectSpectrum Occupanciesen_US
dc.titleCompressed spectrum sensing using sparse recovery convergence patterns through machine learning classificationen_US
dc.typeconferenceObjecten_US
dc.relation.journal30th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)en_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümüen_US
dc.authorid0000-0003-3375-0310en_US
dc.authorid0000-0001-9474-7372en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1109/PIMRC.2019.8904321en_US


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