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This paper studies the problem of support recovery of sparse signals based onmultiple measurement vectors (MMV). The MMV support recovery problem isconnected to the problem of decoding messages in a Single-Input Multiple-Output(SIMO) multiple access channel (MAC), thereby enabling an information theoreticframework for analyzing performance limits in recovering the support of sparsesignals. Sharp sufficient and necessary conditions for successful supportrecovery are derived in terms of the number of measurements per measurementvector, the number of nonzero rows, the measurement noise level, and especiallythe number of measurement vectors. Through the interpretations of the results,in particular the connection to the multiple output communication system, thebenefit of having MMV for sparse signal recovery is illustrated providing atheoretical foundation to the performance improvement enabled by MMV asobserved in many existing simulation results. In particular, it is shown thatthe structure (rank) of the matrix formed by the nonzero entries plays animportant role on the performance limits of support recovery.

Support Recovery of Sparse Signals in the Presence of Multiple Measurement Vectors