  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.
