Journal Article10.1587/TRANSFUN.E100.A.2026
Parameterized L1-Minimization Algorithm for Off-the-Gird Spectral Compressive Sensing
Wei Zhang,Feng Yu +1 more
1
About: This article is published in IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences. The article was published on 01 Sep 2017. The article focuses on the topics: Parameterized complexity.
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Radio Techniques Incorporating Sparse Modeling
TL;DR: The outline of the sparse DOA estimation method is described, and it is shown that the technique can reduce the downlink pilot symbols, and an SBL-based channel estimation method can mitigate this issue.
References
Compressed Sensing Off the Grid
TL;DR: This paper investigates the problem of estimating the frequency components of a mixture of s complex sinusoids from a random subset of n regularly spaced samples and proposes an atomic norm minimization approach to exactly recover the unobserved samples and identify the unknown frequencies.
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Sensitivity to Basis Mismatch in Compressed Sensing
TL;DR: This paper establishes achievable bounds for the l1 error of the best k -term approximation and derives bounds, with similar growth behavior, for the basis pursuit l1 recovery error, indicating that the sparse recovery may suffer large errors in the presence of basis mismatch.
Sensitivity to basis mismatch in compressed sensing
Yuejie Chi,Ali Pezeshki,Louis L. Scharf,Robert Calderbank +3 more
- 14 Mar 2010
TL;DR: This paper establishes achievable bounds for the l1 error of the best k -term approximation and derives bounds, with similar growth behavior, for the basis pursuit l1 recovery error, indicating that the sparse recovery may suffer large errors in the presence of basis mismatch.
•Posted Content
Compressed Sensing off the Grid
TL;DR: In this article, the frequency components of a mixture of s complex sinusoids from a random subset of n regularly spaced samples are estimated using an atomic norm minimization approach to exactly recover the unobserved samples.
704
Random Projections of Smooth Manifolds
TL;DR: A new approach for nonadaptive dimensionality reduction of manifold-modeled data is proposed, demonstrating that a small number of random linear projections can preserve key information about a manifold- modeled signal.
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