Abstract: A greedy algorithm used for the recovery of sparse signals, multiple orthogonal least squares (MOLS) have recently attracted quite a big of attention. In this paper, we consider the number of iterations required for the MOLS algorithm for recovery of a $K$-sparse signal $\mathbf{x}\in\mathbb{R}^n$. We show that MOLS provides stable reconstruction of all $K$-sparse signals $\mathbf{x}$ from $\mathbf{y}=\mathbf{A}\mathbf{x}+\mathbf{w}$ in $\lceil\frac{6K}{M}\rceil$ iterations when the matrix $\mathbf{A}$ satisfies the restricted isometry property (RIP) with isometry constant $\delta_{7K}\leq0.094$. Compared with the existing results, our sufficient condition is not related to the sparsity level $K$.

Key words: Sparse signal recovery, multiple orthogonal least squares (MOLS), sufficient condition, restricted isometry property (RIP)