An Efficient Quasi-Maximum Likelihood Decoding for Finite Constellations

In Multi-Input Multi-Output (MIMO) systems, maximum-likelihood (ML) decoding is equivalent to finding the closest lattice point in an N -dimensional complex space. In general, this algorithm is known to be NP hard. In this paper, we propose a quasi-maximum likelihood algorithm based on Semi-Definite Programming (SDP). We introduce several SDP relaxation models for MIMO systems, with increasing complexity. The resulting algorithms built on these models have near-ML performances with polynomial computational complexities.