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• W1676074242 abstract "Compressed sensing (CS) demonstrates that sparse signals can be recovered from underdetermined linear measurements. We focus on the joint sparse recovery problem where multiple signals share the same common sparse support sets, and they are measured through the same sensing matrix. Leveraging a recent information theoretic characterization of single signal CS, we formulate the optimal minimum mean square error (MMSE) estimation problem, and derive a belief propagation algorithm, its relaxed version, for the joint sparse recovery problem and an approximate message passing algorithm. In addition, using density evolution, we provide a sufficient condition for exact recovery." @default.
• W1676074242 created "2016-06-24" @default.
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• W1676074242 date "2011-02-16" @default.
• W1676074242 modified "2023-10-01" @default.
• W1676074242 title "Belief propagation for joint sparse recovery" @default.
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