Graph Neural Network-based End-to-End Learning for Multi-User MIMO Systems

TL;DR

Simulation results demonstrate that the proposed E2E outperforms existing schemes with a predefined modulator and achieves an approximate 2 dB gain in a high MUI environment and surpasses even the maximum-likelihood detector in a low MUI condition.

Abstract

End-to-end (E2E) learning has recently been proposed to jointly design the modulator and symbol detector by using deep neural networks (DNNs). However, existing schemes lack sufficient capability to cancel multi-user interference (MUI) in uplink multi-user multiple-input multiple-output (MU-MIMO) systems. In this paper, we propose a graph neural network (GNN)-based E2E learning scheme that employs a GNN-based modulator to generate learned constellation points, and a GNN-based detector to cancel MUI. They are jointly optimized to minimize the symbol error rate (SER) performance loss. Simulation results demonstrate that the proposed E2E outperforms existing schemes with a predefined modulator. Specifically, it achieves an approximate 2 dB gain in a high MUI environment and surpasses even the maximum-likelihood (ML) detector in a low MUI condition.

Figures (4)

• Figure 1: Proposed E2E architecture for offline training
• Figure 2: Complexity comparison
• Figure 3: Comparison of constellation $\hat{\boldsymbol{\Omega}}$ (${\color{red}\circ}$) and $\boldsymbol{\Omega}$ (${\color{black}\circ}$) in different MUI environments.
• Figure 4: SER performance comparison in different MUI environments