Detection by Sampling: Massive MIMO Detector based on Langevin Dynamics

TL;DR

This paper introduces a novel MIMO detection method using Langevin dynamics, which approximates optimal symbol detection efficiently and achieves state-of-the-art error rates in simulations.

Contribution

It proposes a new sampling-based MIMO detector based on annealed Langevin dynamics that effectively balances accuracy and computational complexity.

Findings

Achieves state-of-the-art symbol error rate performance

Provides a probabilistic sampling approach to MIMO detection

Balances detection accuracy with computational efficiency

Abstract

Optimal symbol detection in multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Hence, the objective of any detector of practical relevance is to get reasonably close to the optimal solution while keeping the computational complexity in check. In this work, we propose a MIMO detector based on an annealed version of Langevin (stochastic) dynamics. More precisely, we define a stochastic dynamical process whose stationary distribution coincides with the posterior distribution of the symbols given our observations. In essence, this allows us to approximate the maximum a posteriori estimator of the transmitted symbols by sampling from the proposed Langevin dynamic. Furthermore, we carefully craft this stochastic dynamic by gradually adding a sequence of noise with decreasing variance to the trajectories, which ensures that the estimated symbols belong to a pre-specified discrete constellation. Through numerical experiments, we show that our proposed detector yields state-of-the-art symbol error rate performance.