  In this paper, we address the theoretical limitations in reconstructing
sparse signals (in a known complete basis) using compressed sensing framework.
We also divide the CS to non-blind and blind cases. Then, we compute the
Bayesian Cramer-Rao bound for estimating the sparse coefficients while the
measurement matrix elements are independent zero mean random variables.
Simulation results show a large gap between the lower bound and the performance
of the practical algorithms when the number of measurements are low.
