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- Title
Revisiting the RIP of Real and Complex Gaussian Sensing Matrices Through RIV Framework.
- Authors
James, Oliver
- Abstract
In this paper, we aim to revisit the restricted isometry property of real and complex Gaussian sensing matrices. We do this reconsideration via the recently introduced restricted isometry random variable (RIV) framework for the real Gaussian sensing matrices. We first generalize the RIV framework to the complex settings and illustrate that the restricted isometry constants (RICs) of complex Gaussian sensing matrices are smaller than their real-valued counterpart. The reasons behind the better RIC nature of complex sensing matrices over their real-valued counterpart are delineated. We also demonstrate via critical functions, upper bounds on the RICs, that complex Gaussian matrices with prescribed RICs exist for larger number of problem sizes than the real Gaussian matrices.
- Subjects
COMPRESSED sensing; SIGNAL sampling; IRREGULAR sampling (Signal processing); EXTREME value theory; RESTRICTED isometry property
- Publication
Wireless Personal Communications, 2016, Vol 87, Issue 2, p513
- ISSN
0929-6212
- Publication type
Article
- DOI
10.1007/s11277-015-3083-x