Compressive Sensing with Optimal Sparsifying Basis and Applications in Spectrum Sensing

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Compressive Sensing with Optimal Sparsifying Basis and Applications in Spectrum Sensing

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Title: Compressive Sensing with Optimal Sparsifying Basis and Applications in Spectrum Sensing
Author: Vlah, Dario; Kung, H.T. T.; Gwon, Youngjune Lee

Note: Order does not necessarily reflect citation order of authors.

Citation: Gwon, Youngjune, H.T. Kung, and Dario Vlah. 2012. Compressive sensing with optimal sparsifying basis and applications in spectrum sensing. Paper presented at IEEE 2012 Global Telecommunications Conference (GLOBECOM 2012), Annaheim, CA, December 3-7, 2012.
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Abstract: We describe a method of integrating Karhunen-Loève Transform (KLT) into compressive sensing, which can as a result improve the compression ratio without affecting the accuracy of decoding. We present two complementary results: 1) by using KLT to find an optimal basis for decoding we can drastically reduce the number of measurements for compressive sensing used in applications such as radio spectrum analysis; 2) by using compressive sensing we can estimate and recover the KLT basis from compressive measurements of an input signal. In particular, we propose CS-KLT, an online estimation algorithm to cope with nonstationarity of wireless channels in reality. We validate our results with empirical data collected from a wideband UHF spectrum and eld experiments to detect multiple radio transmitters, using software-defined radios.
Other Sources: http://www.eecs.harvard.edu/~htk/publication/2012-globecom-gwon-kung-vlah.pdf
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:10000991

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  • FAS Scholarly Articles [7175]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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