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SPARSITY-UNDERSAMPLING TRADEOFF OF COMPRESSED SENSING IN THE COMPLEX DOMAIN

Compressed Sensing: Theory and Methods

Full Paper at IEEE Xplore

Presented by: Zai Yang, Author(s): Zai Yang, Cishen Zhang, Nanyang Technological University, Singapore

In this paper, recently developed ONE-L1 algorithms for compressed sensing are applied to complex-valued signals and sampling matrices. The optimal and iterative solution of ONE-L1 algorithms enables empirical investigation and evaluation of the sparsity-undersampling tradeoff of $ell_1$ minimization of complex-valued signals. A remarkable finding is that, not only there exists a sharp phase transition for the complex case determining the behavior of the sparsity-undersampling tradeoff, but also this phase transition is different and superior to that for the real case, providing a significantly improved success phase in the transition plane.

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  Lecture Information

Recorded: 2011-05-25 14:45 - 15:05, Club B
Added: 22. 6. 2011 03:51
Number of views: 61
Video resolution: 1024x576 px, 512x288 px
Video length: 0:15:34
Audio track: MP3 [5.24 MB], 0:15:34