论文标题
边缘图神经网络用于大规模MIMO检测
Edge Graph Neural Networks for Massive MIMO Detection
论文作者
论文摘要
大量多输入多重输出（MIMO）检测是现代无线通信系统中的重要问题。尽管传统的信仰传播（BP）检测器在循环图上的表现较差，但最新的图神经网络（GNNS）的方法可以克服BP的缺点并实现出色的性能。然而，直接使用GNN忽略了边缘属性的重要性，并且使用完全连接的图形结构遭受了高度计算开销。在本文中，我们提出了一种有效的GNN启发算法，称为边缘图神经网络（EGNN），以检测MIMO信号。我们首先通过通道相关来计算图形边缘权重，然后利用所获得的权重作为度量，以评估每个节点的邻居的重要性。此外，我们设计了一种自适应边缘滴（ED）方案来稀疏图，以便可以大大降低计算成本。实验结果表明，与基于GNN的方法相比，我们提出的EGNN与流行的MIMO检测方法的性能更好或可比的表现，并且在不同调制方案中的检测时间最少。
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.
