Near-optimal stochastic MIMO signal detection with a mixture of t-distribution prior

TL;DR

This paper introduces a novel MIMO signal detection method using a mixture of t-distributions as priors, leveraging Hamiltonian Monte Carlo to achieve near-optimal performance with polynomial complexity, advancing future wireless communication systems.

Contribution

It extends previous normal mixture priors to t-distributions for improved detection, demonstrating near-optimal results with efficient computation.

Findings

Achieves near-optimal detection performance

Uses polynomial computational complexity

Enhances MIMO detection for 6G networks

Abstract

Multiple-input multiple-output (MIMO) systems will play a crucial role in future wireless communication, but improving their signal detection performance to increase transmission efficiency remains a challenge. To address this issue, we propose extending the discrete signal detection problem in MIMO systems to a continuous one and applying the Hamiltonian Monte Carlo method, an efficient Markov chain Monte Carlo algorithm. In our previous studies, we have used a mixture of normal distributions for the prior distribution. In this study, we propose using a mixture of t-distributions, which further improves detection performance. Based on our theoretical analysis and computer simulations, the proposed method can achieve near-optimal signal detection with polynomial computational complexity. This high-performance and practical MIMO signal detection could contribute to the development of the 6th-generation mobile network.