Edge Graph Neural Networks for Massive MIMO Detection

TL;DR

This paper introduces EGNN, an efficient graph neural network-based algorithm for massive MIMO detection that leverages edge attributes and adaptive sparsification to improve performance and reduce computational costs.

Contribution

The paper proposes a novel EGNN method that incorporates edge weights and adaptive edge dropping, enhancing MIMO detection efficiency and accuracy over existing GNN approaches.

Findings

EGNN outperforms traditional methods in detection accuracy.

EGNN achieves comparable or better performance than existing GNN-based detectors.

EGNN significantly reduces computational time through adaptive sparsification.

Abstract

Massive Multiple-Input Multiple-Out (MIMO) detection is an important problem in modern wireless communication systems. While traditional Belief Propagation (BP) detectors perform poorly on loopy graphs, the recent Graph Neural Networks (GNNs)-based method can overcome the drawbacks of BP and achieve superior performance. Nevertheless, direct use of GNN ignores the importance of edge attributes and suffers from high computation overhead using a fully connected graph structure. In this paper, we propose an efficient GNN-inspired algorithm, called the Edge Graph Neural Network (EGNN), to detect MIMO signals. We first compute graph edge weights through channel correlation and then leverage the obtained weights as a metric to evaluate the importance of neighbors of each node. Moreover, we design an adaptive Edge Drop (ED) scheme to sparsify the graph such that computational cost can be significantly reduced. Experimental results demonstrate that our proposed EGNN achieves better or comparable performance to popular MIMO detection methods for different modulation schemes and costs the least detection time compared to GNN-based approaches.