A Near Maximum Likelihood Decoding Algorithm for MIMO Systems Based on Semi-Definite Programming

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 problem is known to be NP hard. In this paper, the authors propose a quasi-maximum likelihood algorithm based on Semi-Definite Programming (SDP). They introduce several SDP relaxation models for MIMO systems, with increasing complexity. They use interior-point methods for solving the models and obtain a near-ML performance with polynomial computational complexity. Lattice basis reduction is applied to further reduce the computational complexity of solving these models. The proposed relaxation models are also used for soft output decoding in MIMO systems.