Compressed spectrum sensing using sparse recovery convergence patterns through machine learning classification
| dc.authorid | 0000-0003-3375-0310 | |
| dc.authorid | 0000-0001-9474-7372 | |
| dc.contributor.author | Nazzal, Mahmoud | |
| dc.contributor.author | Hasekio?lu, Orkun | |
| dc.contributor.author | Ekti, Ali Rıza | |
| dc.contributor.author | Görçin, Ali | |
| dc.contributor.author | Arslan, Hüseyin | |
| dc.date.accessioned | 2020-01-02T07:37:33Z | |
| dc.date.available | 2020-01-02T07:37:33Z | |
| dc.date.issued | 2019 | |
| dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | |
| dc.description.abstract | Despite 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. | |
| dc.identifier.citation | Nazzal, M., Hasekio?lu, 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.2019.8904321 | |
| dc.identifier.doi | 10.1109/PIMRC.2019.8904321 | |
| dc.identifier.isbn | 9781538681107 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.uri | http://doi.org/10.1109/PIMRC.2019.8904321 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12511/4849 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.ispartof | 30th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/embargoedAccess | |
| dc.subject | Machine Learning | |
| dc.subject | Compressed Spectrum Sensing | |
| dc.subject | Spectrum Occupancies | |
| dc.title | Compressed spectrum sensing using sparse recovery convergence patterns through machine learning classification | |
| dc.type | Conference Object |
