Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Support Recovery of Sparse Signals in the Presence of Multiple
  Measurement Vectors   This paper studies the problem of support recovery of sparse signals based on
multiple measurement vectors (MMV). The MMV support recovery problem is
connected to the problem of decoding messages in a Single-Input Multiple-Output
(SIMO) multiple access channel (MAC), thereby enabling an information theoretic
framework for analyzing performance limits in recovering the support of sparse
signals. Sharp sufficient and necessary conditions for successful support
recovery are derived in terms of the number of measurements per measurement
vector, the number of nonzero rows, the measurement noise level, and especially
the number of measurement vectors. Through the interpretations of the results,
in particular the connection to the multiple output communication system, the
benefit of having MMV for sparse signal recovery is illustrated providing a
theoretical foundation to the performance improvement enabled by MMV as
observed in many existing simulation results. In particular, it is shown that
the structure (rank) of the matrix formed by the nonzero entries plays an
important role on the performance limits of support recovery.

15
The categories of this paper are: nucl-ex
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The categories of this paper are: econ
30
The categories of this paper are: q-alg
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The categories of this paper are: eess
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
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The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
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The categories of this paper are: solv-int
3
The categories of this paper are: physics
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The categories of this paper are: adap-org
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ex
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The categories of this paper are: funct-an