Spectrum occupancy reconstruction in distributed cognitive radio networks using deep learning

dc.contributor.authorHlophe, Mduduzi Comfort
dc.contributor.authorMaharaj, Bodhaswar Tikanath Jugpershad
dc.contributor.emailu16250444@tuks.co.zaen_ZA
dc.date.accessioned2020-02-07T11:55:44Z
dc.date.available2020-02-07T11:55:44Z
dc.date.issued2019
dc.description.abstractSpectrum occupancy reconstruction is an important issue often encountered in collaborative spectrum sensing in distributed cognitive radio networks (CRNs). This issue arises when the spectrum sensing data that are collaborated by secondary users have gaps of missing entries. Many data imputation techniques, such as matrix completion techniques, have shown great promise in dealing with missing spectrum sensing observations by reconstructing the spectrum occupancy data matrix. However, matrix completion approaches achieve lower reconstruction resolution due to the use of standard singular value decomposition (SVD), which is designed for more general matrices. In this paper, we consider the problem of spectrum occupancy reconstruction where the spectrum sensing results across the CRN are represented as a plenary grid on a Markov random eld. We formulate the problem as a magnetic excitation state recovery problem, and the stochastic gradient descent (SGD) method is applied to solve the matrix factorization. SGD is able to learn and impute the missing values with a low reconstruction error compared with SVD. The graphical and numerical results show that the SGD algorithm competes favorably SVD in the matrix factorization by taking advantage of correlations in multiple dimensions.en_ZA
dc.description.departmentElectrical, Electronic and Computer Engineeringen_ZA
dc.description.librarianam2020en_ZA
dc.description.sponsorshipThe Association of Commonwealth Universities under Grant FE-2015-26, and in part by the Sentech Chair in Broadband Wireless Multimedia Communications.en_ZA
dc.description.urihttp://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639en_ZA
dc.identifier.citationHlophe, M.C. & Maharaj, S.B.T. 2019, 'Spectrum occupancy reconstruction in distributed cognitive radio networks using deep learning', IEEE Access, vol. 7, pp. 14294-14307.en_ZA
dc.identifier.issn2169-3536 (online)
dc.identifier.other10.1109/ACCESS.2019.2894784
dc.identifier.urihttp://hdl.handle.net/2263/73151
dc.language.isoenen_ZA
dc.publisherInstitute of Electrical and Electronics Engineersen_ZA
dc.rights© 2019 IEEEen_ZA
dc.subjectIsing modelen_ZA
dc.subjectMatrix factorizationen_ZA
dc.subjectMetropolis-Hastings algorithmen_ZA
dc.subjectMissing valuesen_ZA
dc.subjectCognitive radio network (CRN)en_ZA
dc.subjectStochastic gradient descent (SGD)en_ZA
dc.subjectSingular value decomposition (SVD)en_ZA
dc.titleSpectrum occupancy reconstruction in distributed cognitive radio networks using deep learningen_ZA
dc.typeArticleen_ZA

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