I. Introduction
Recently, prosperous works in compressed sensing (CS) [1][2] show that an accurate recovery can be achieved by sampling signal at a rate proportional to its underlying “information content” rather than bandwidth. The key improvement of CS is that the sampling rate can be significantly reduced below Nyquist rate by replacing the uniform sampling with linear measurement, when the signals are sparse or compressible on certain dictionary. In particular, CS dedicates to rebuild a sparse signal from its linear measurements by solving an underdetermined system. \min\limits_{x\in {\BBR}^{n}}\Vert x\Vert_{p}\quad s.t.\ y=\Phi x, \quad (0\leq p < 2)
where is the sensing matrix allowing and fulfilling the restricted isometry property (RIP) or incoherence condition, and norm encourages sparsity. It has been proved that a number of random matrix ensembles satisfy RIP well.