GNN-Enhanced Approximate Message Passing for Massive/Ultra-Massive MIMO Detection

TL;DR

This paper introduces AMP-GNN, a novel low-complexity detection algorithm for massive MIMO systems that combines approximate message passing with graph neural networks, significantly improving performance and robustness.

Contribution

It develops a GNN-enhanced AMP algorithm specifically designed for massive MIMO detection, offering improved accuracy and reduced complexity over existing deep learning methods.

Findings

Significantly improves AMP detector performance

Achieves comparable results to state-of-the-art deep learning detectors

Exhibits strong robustness to user number variations

Abstract

Efficient massive/ultra-massive multiple-input multiple-output (MIMO) detection algorithms with satisfactory performance and low complexity are critical to meet the high throughput and ultra-low latency requirements in 5G and beyond communications, given the extremely large number of antennas. In this paper, we propose a low-complexity graph neural network (GNN) enhanced approximate message passing (AMP) algorithm, AMP-GNN, for massive/ultra-massive MIMO detection. The structure of the neural network is customized by unfolding the AMP algorithm and introducing the GNN module for multiuser interference cancellation. Numerical results will show that the proposed AMP-GNN significantly improves the performance of the AMP detector and achieves comparable performance as the state-of-the-art deep learning-based MIMO detectors but with reduced computational complexity. Furthermore, it presents strong robustness to the change of the number of users.