  We analyze the asymptotic performance of sparse signal recovery from noisy
measurements. In particular, we generalize some of the existing results for the
Gaussian case to subgaussian and other ensembles. An achievable result is
presented for the linear sparsity regime. A converse on the number of required
measurements in the sub-linear regime is also presented, which cover many of
the widely used measurement ensembles. Our converse idea makes use of a
correspondence between compressed sensing ideas and compound channels in
information theory.
