  In this paper a new result of recovery of sparse vectors from deterministic
and noisy measurements by l1 minimization is given. The sparse vector is
randomly chosen and follows a generic p-sparse model introduced by Candes and
al. The main theorem ensures consistency of l1 minimization with high
probability. This first result is secondly extended to compressible vectors.
