New Iterative Detector of MIMO Transmission Using Sparse Decomposition

This paper addresses the problem of decoding in large-scale multiple-input–multiple-output (MIMO) systems. In this case, the optimal maximum-likelihood (ML) detector becomes impractical due to an exponential increase in the complexity with the signal and the constellation dimensions. This paper 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
$\ell_{1} $
-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.