论文标题
大型MIMO网络的基于图神经网络的渠道跟踪
Graph Neural Network based Channel Tracking for Massive MIMO Networks
论文作者
论文摘要
在本文中，我们求助于图形神经网络（GNN），并在高移动性方案下为大量多输入多输出网络提出了新的通道跟踪方法。我们首先利用少量飞行员来实现初始通道估计。然后，我们以图形形式表示获得的通道数据，并通过沿图的边缘来描述通道空间相关性。此外，我们介绍了GNN主单元的计算步骤，并设计了基于GNN的通道跟踪框架，其中包括编码器，核心网络和解码器。模拟结果证实了我们提出的基于GNN的方案可以比Feelforward神经网络的作品更好地实现性能。
In this paper, we resort to the graph neural network (GNN) and propose the new channel tracking method for the massive multiple-input multiple-output networks under the high mobility scenario. We first utilize a small number of pilots to achieve the initial channel estimation. Then, we represent the obtained channel data in the form of graphs and describe the channel spatial correlation by the weights along the edges of the graph. Furthermore, we introduce the computation steps of the main unit for the GNN and design a GNN-based channel tracking framework, which includes an encoder, a core network and a decoder. Simulation results corroborate that our proposed GNN-based scheme can achieve better performance than the works with feedforward neural network.
