论文标题
图形神经网络用于大规模MIMO检测
Graph Neural Networks for Massive MIMO Detection
论文作者
论文摘要
在本文中，我们创新使用图形神经网络（GNN）来学习一个消息解决方案，以用于在无线通信中大量多重多输入多输出（MIMO）检测的推理任务。我们采用基于马尔可夫随机场（MRF）的图形模型，当信仰传播（BP）在传输符号上假定均匀的先验时，它会产生较差的结果。数值模拟表明，在统一的先验假设下，我们的基于GNN的MIMO检测解决方案优于BP相比，我们的最小均值误差（MMSE）基线检测器优于均值误差（MMSE）基线检测器。此外，实验表明，算法的性能通过将MMSE信息纳入先验而略有改进。
In this paper, we innovately use graph neural networks (GNNs) to learn a message-passing solution for the inference task of massive multiple multiple-input multiple-output (MIMO) detection in wireless communication. We adopt a graphical model based on the Markov random field (MRF) where belief propagation (BP) yields poor results when it assumes a uniform prior over the transmitted symbols. Numerical simulations show that, under the uniform prior assumption, our GNN-based MIMO detection solution outperforms the minimum mean-squared error (MMSE) baseline detector, in contrast to BP. Furthermore, experiments demonstrate that the performance of the algorithm slightly improves by incorporating MMSE information into the prior.
