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.

本文采用图神经网络方法，提出了一种适用于高速移动场景下的大规模 MIMO 网络的新型信道跟踪方法，通过少量导频进行初步的信道估计，将所得数据以图的形式表示并通过图上的边权来描述信道空间相关性，引入了主要单元的计算过程并设计了一个基于图神经网络的信道跟踪框架，包括编码器，核心网络和解码器 ，仿真结果证实了我们提出的基于 GNN 的方案比采用前馈神经网络方法的方案表现更好。