This paper addresses the problem of decoding in large scale MIMO systems. In this case, the optimal maximum likelihood detector becomes impractical due to an exponential increase of the complexity with the signal and the constellation dimensions. Our work introduces an iterative decoding strategy with a tolerable complexity order. We consider a MIMO system with finite constellation and model it as a system with sparse signal sources. We propose an ML relaxed detector that minimizes the euclidean distance with the received signal while preserving a constant l1-norm of the decoded signal. We also show that the detection problem is equivalent to a convex optimization problem which is solvable in polynomial time. Two applications are proposed, and simulation results illustrate the efficiency of the proposed detector.