Abstract-Letbe an by matrix ( ) which is an instance of a real random Gaussian ensemble. In compressed sensing we are interested in finding the sparsest solution to the system of equations x = y for a given y. In general, whenever the sparsity of x is smaller than half the dimension of y then with overwhelming probability over the sparsest solution is unique and can be found by an exhaustive search over x with an exponential time complexity for any y. The recent work of Candés, Donoho, and Tao shows that minimization of the 1 norm of x subject to x = y results in the sparsest solution provided the sparsity of x, say , is smaller than a certain threshold for a given number of measurements. Specifically, if the dimension of y approaches the dimension of x, the sparsity of x should be 0 239 N. Here, we consider the case where x is block sparse, i.e., x consists of = blocks where each block is of length and is either a zero vector or a nonzero vector (under nonzero vector we consider a vector that can have both, zero and nonzero components). Instead of 1 -norm relaxation, we consider the following relaxation: min x X 1 2 + X 2 2 + + X 2 subject to x = y (*) where X = (x ( 1) +1 x ( 1) +2 . . . x ) for = 1 2 . . .
. Our main result is that as, (*) finds the sparsest solution to x = y, with overwhelming probability in , for any x whose sparsity is(1 2) ( ), provided 1 1 , and = (log(1 ) 3 ). The relaxation given in (*) can be solved in polynomial time using semi-definite programming.Index Terms-Compressed sensing, block-sparse signals, semidefinite programming.