MIMO Detection via Gaussian Mixture Expectation Propagation: A Bayesian Machine Learning Approach for High-Order High-Dimensional MIMO Systems

TL;DR

This paper introduces a Bayesian machine learning-based MIMO detection algorithm using Gaussian Mixture Expectation Propagation, significantly improving detection accuracy in high-dimensional systems while maintaining low complexity.

Contribution

It proposes a novel GMM-based EP algorithm that enhances prior approximation and outperforms existing detection methods in high-order MIMO systems.

Findings

Outperforms state-of-the-art detection algorithms

Maintains low computational complexity

Improves prior approximation with GMM messages

Abstract

MIMO systems can simultaneously transmit multiple data streams within the same frequency band, thus exploiting the spatial dimension to enhance performance. MIMO detection poses considerable challenges due to the interference and noise introduced by the concurrent transmission of multiple streams. Efficient Uplink (UL) MIMO detection algorithms are crucial for decoding these signals accurately and ensuring robust communication. In this paper a MIMO detection algorithm is proposed which improves over the Expectation Propagation (EP) algorithm. The proposed algorithm is based on a Gaussian Mixture Model (GMM) approximation for Belief Propagation (BP) and EP messages. The GMM messages better approximate the data prior when EP fails to do so and thus improve detection. This algorithm outperforms state of the art detection algorithms while maintaining low computational complexity.