Graph Neural Network Aided MU-MIMO Detectors

TL;DR

This paper introduces graph neural network-based detectors for MU-MIMO systems that significantly improve detection performance and robustness against multi-user interference, especially in dynamic user scenarios.

Contribution

It develops a GNN framework to enhance message passing detectors and proposes two novel neural network detectors, GEPNet and GPICNet, with proven permutation equivariance.

Findings

GEPNet approaches maximum likelihood performance.

GPICNet doubles the multiplexing gain of previous detectors.

Detectors are robust to dynamic user number changes.

Abstract

Multi-user multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks. A base station serves many users in an uplink MU-MIMO system, leading to a substantial multi-user interference (MUI). Designing a high-performance detector for dealing with a strong MUI is challenging. This paper analyses the performance degradation caused by the posterior distribution approximation used in the state-of-the-art message passing (MP) detectors in the presence of high MUI. We develop a graph neural network based framework to fine-tune the MP detectors' cavity distributions and thus improve the posterior distribution approximation in the MP detectors. We then propose two novel neural network based detectors which rely on the expectation propagation (EP) and Bayesian parallel interference cancellation (BPIC), referred to as the GEPNet and GPICNet detectors, respectively. The GEPNet detector maximizes detection performance, while GPICNet detector balances the performance and complexity. We provide proof of the permutation equivariance property, allowing the detectors to be trained only once, even in the systems with dynamic changes of the number of users. The simulation results show that the proposed GEPNet detector performance approaches maximum likelihood performance in various configurations and GPICNet detector doubles the multiplexing gain of BPIC detector.