Hamiltonian Monte Carlo-Based Near-Optimal MIMO Signal Detection

TL;DR

This paper introduces a novel MIMO signal detection method using Hamiltonian Monte Carlo that transforms the discrete detection problem into a continuous one, achieving near-optimal performance with polynomial complexity, suitable for 6G systems.

Contribution

It proposes a continuous reformulation of MIMO detection using Hamiltonian Monte Carlo and introduces a mixture of t-distributions and horseshoe density to enhance detection performance.

Findings

Achieves near-optimal detection performance in simulations.

Maintains polynomial computational complexity.

Potential to accelerate 6G mobile communication development.

Abstract

Multiple-input multiple-output (MIMO) technology is essential for the optimal functioning of next-generation wireless networks; however, enhancing its signal-detection performance for improved spectral efficiency is challenging. Here, we propose an approach that transforms the discrete MIMO detection problem into a continuous problem while leveraging the efficient Hamiltonian Monte Carlo algorithm. For this continuous framework, we employ a mixture of t-distributions as the prior distribution. To improve the performance in the coded case further, we treat the likelihood's temperature parameter as a random variable and address its optimization. This treatment leads to the adoption of a horseshoe density for the likelihood. Theoretical analysis and extensive simulations demonstrate that our method achieves near-optimal detection performance while maintaining polynomial computational complexity. This MIMO detection technique can accelerate the development of 6G mobile communication systems.