Edge Graph Neural Networks for Massive MIMO Detection
This work addresses efficient signal detection in wireless communication systems, representing an incremental improvement over prior GNN-based methods.
The paper tackled the problem of massive MIMO detection in wireless communication by proposing an Edge Graph Neural Network (EGNN) that incorporates edge attributes and sparsifies the graph, achieving better or comparable performance to existing methods and reducing detection time compared to GNN-based approaches.
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.