  Multiple measurement vector (MMV) problem addresses the recovery of a set of
sparse signal vectors that share common non-zero support, and has emerged an
important topics in compressed sensing. Even though the fundamental performance
limit of recoverable sparsity level has been formally derived, conventional
algorithms still exhibit significant performance gaps from the theoretical
bound. The main contribution of this paper is a novel concatenate MMV and boost
(CoMBo) algorithm that achieves the theoretical bound. More specifically, the
algorithm concatenates MMV to a larger dimensional SMV problem and boosts it by
multiplying random orthonormal matrices. Extensive simulation results
demonstrate that CoMBo outperforms all existing methods and achieves the
theoretical bound as the number of measurement vector increases.
