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

TL;DR

This paper presents a semi-definite programming based algorithm for near-maximum likelihood decoding in MIMO systems, achieving high accuracy with polynomial complexity and enhanced soft output decoding.

Contribution

It introduces novel SDP relaxation models for MIMO decoding, combining interior-point methods and lattice reduction to improve efficiency and performance.

Findings

Achieves near-ML performance with polynomial complexity

Introduces multiple SDP relaxation models for MIMO decoding

Enhances soft output decoding using the proposed models

Abstract

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, 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. We 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.