Near-Optimal MIMO Detection Using Gradient-Based MCMC in Discrete Spaces

TL;DR

This paper introduces a novel gradient-based MCMC algorithm for MIMO detection that guarantees convergence, achieves near-optimal performance, and is scalable for large systems, advancing wireless communication capabilities.

Contribution

The paper presents a new theoretically grounded sampling algorithm for discrete spaces, enabling near-optimal MIMO detection with proven convergence and scalability.

Findings

Achieves near-optimal detection performance.

Outperforms existing state-of-the-art methods.

Scalable to large MIMO systems.

Abstract

The discrete nature of transmitted symbols poses challenges for achieving optimal detection in multiple-input multiple-output (MIMO) systems associated with a large number of antennas. Recently, the combination of two powerful machine learning methods, Markov chain Monte Carlo (MCMC) sampling and gradient descent, has emerged as a highly efficient solution to address this issue. However, existing gradient-based MCMC detectors are heuristically designed and thus are theoretically untenable. To bridge this gap, we introduce a novel sampling algorithm tailored for discrete spaces. This algorithm leverages gradients from the underlying continuous spaces for acceleration while maintaining the validity of probabilistic sampling. We prove the convergence of this method and also analyze its convergence rate using both MCMC theory and empirical diagnostics. On this basis, we develop a MIMO detector that precisely samples from the target discrete distribution and generates posterior Bayesian estimates using these samples, whose performance is thereby theoretically guaranteed. Furthermore, our proposed detector is highly parallelizable and scalable to large MIMO dimensions, positioning it as a compelling candidate for next-generation wireless networks. Simulation results show that our detector achieves near-optimal performance, significantly outperforms state-of-the-art baselines, and showcases resilience to various system setups.