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Rank Awareness in Joint Sparse Recovery [PDF]

IEEE Transactions on Information Theory, 2012
This paper revisits the sparse multiple measurement vector (MMV) problem, where the aim is to recover a set of jointly sparse multichannel vectors from incomplete measurements. This problem is an extension of single channel sparse recovery, which lies at
Mike E Davies, Yonina Eldar
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Jointly Sparse Signal Recovery and Support Recovery via Deep Learning With Applications in MIMO-Based Grant-Free Random Access

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Sparse Recovery Using Sparse Matrices

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Block-Sparse Signals: Uncertainty Relations and Efficient Recovery

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Average Case Analysis of Multichannel Sparse Recovery Using Convex Relaxation

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Pushing the Limits of Sparse Support Recovery Using Correlation Information

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Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-Rank Matrices

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General RIP bounds of δ for sparse signals recovery by ℓ minimization

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