Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDeshmukh, Siddharth-
dc.contributor.authorBhuyan, Snigdha-
dc.identifier.citation10th International Conference on Communication and Networks (COMSNETS) 2018, Bangalore, India, 3 – 7 January, 2018en_US
dc.descriptionCopyright of the document belongs to proceedings publisher.en_US
dc.description.abstractIn this paper, we present a cooperative spectrum sensing technique to identify the spectral location of primary users (PUs) in a wideband spectrum. Here we first divide the wideband into narrow bands of equal bandwidth and deploy arbitrarily located cognitive radio (CR) sensors to sense the narrowbands. The CR sensors operate on conventional energy detection principle and we assume that their spectrum sensing range overlap over each other so as to exploit diversity and mitigate deep fade problem. In order to have energy efficiency, we introduce probabilistic active and sleep state for individual CR sensors. CR sensors in active state compute energy in the specified sensing range and communicate it to a fusion center. Assuming sparse occupancy of PUs in the wideband and by Parseval's theorem, we represent energies of sub-bands in form of spare vector. Next, we exploit concept of compressive sensing (CS) at fusion center to reconstruct the vector representing energies in the sub-bands. Since individual CR sensors randomly take active or sleep state in a time epoch, the sensing matrix for reconstruction is identified as random matrix. Extending the analysis, we also investigate the value of probability of active/sleep state for which sensing matrix satisfies Restricted Isometry Property (RIP). Finally, we compare the reconstructed energies of sub-band with a specified threshold to make decision on presence or absence of PU in the particular sub-bands. We validate our approach via simulations in which we show performance with (i) variation in probability of sleep state; (ii) variation in number of time epochs or measurements; (iii) varying degree of overlap in spectrum sensing range.en_US
dc.subjectCognitive radioen_US
dc.subjectWideband spectrum sensingen_US
dc.subjectCompressive sensingen_US
dc.subjectEnergy efficiencyen_US
dc.titleCompressive sensing based energy efficient wideband congnitive radio networken_US
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
2018_COMSNETS_SDeshmukh_Compressive.pdfConference Paper259.34 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.