MIMO Detection for High-Order QAM Based on a Gaussian Tree Approximation

TL;DR

This paper introduces a novel MIMO detection algorithm using Gaussian tree approximation and belief propagation, significantly improving performance and complexity for high-order QAM systems.

Contribution

It presents a new detection method based on Gaussian tree approximation that outperforms existing techniques in MIMO systems with high-order QAM.

Findings

Outperforms current methods in detection accuracy.

Reduces computational complexity compared to traditional algorithms.

Effective for high-order QAM in MIMO systems.

Abstract

This paper proposes a new detection algorithm for MIMO communication systems employing high order QAM constellations. The factor graph that corresponds to this problem is very loopy; in fact, it is a complete graph. Hence, a straightforward application of the Belief Propagation (BP) algorithm yields very poor results. Our algorithm is based on an optimal tree approximation of the Gaussian density of the unconstrained linear system. The finite-set constraint is then applied to obtain a loop-free discrete distribution. It is shown that even though the approximation is not directly applied to the exact discrete distribution, applying the BP algorithm to the loop-free factor graph outperforms current methods in terms of both performance and complexity. The improved performance of the proposed algorithm is demonstrated on the problem of MIMO detection.