Accelerated massive MIMO detector based on annealed underdamped Langevin dynamics

TL;DR

This paper introduces an annealed underdamped Langevin dynamics-based MIMO detector that achieves state-of-the-art symbol error rates with adjustable complexity, outperforming existing methods in accuracy and speed.

Contribution

It presents a novel MIMO detection method using annealed underdamped Langevin dynamics, offering improved performance and computational efficiency over prior overdamped Langevin approaches.

Findings

Lower symbol error rate than competing methods

Reduced computational time compared to overdamped Langevin detector

Flexible tuning of complexity and performance balance

Abstract

We propose a multiple-input multiple-output (MIMO) detector based on an annealed version of the \emph{underdamped} Langevin (stochastic) dynamic. Our detector achieves state-of-the-art performance in terms of symbol error rate (SER) while keeping the computational complexity in check. Indeed, our method can be easily tuned to strike the right balance between computational complexity and performance as required by the application at hand. This balance is achieved by tuning hyperparameters that control the length of the simulated Langevin dynamic. Through numerical experiments, we demonstrate that our detector yields lower SER than competing approaches (including learning-based ones) with a lower running time compared to a previously proposed \emph{overdamped} Langevin-based MIMO detector.