  In this paper we consider the problem of sparse signal recovery in Multiple
Measurement Vectors (MMVs) case. Recently, ample researches have been conducted
to solve this problem and diverse methods are proposed, one of which is deep
neural network approach. Here, employing deep neural networks we have provided
two new greedy algorithms in order to solve MMV problems. In the first
algorithm, we create a stacked vector of measurement matrix columns and a new
measurement matrix, which can be assumed as the Kronecker product of the
primary compressive sampling matrix and a unitary matrix. Afterwards, in order
to reconstruct sparse vectors corresponding to this new set of equations, a
four-layer feed-forward neural network is applied.
