Graph Neural Network-Based Channel Tracking for Massive MIMO Networks

作     者：Yang, Yindi Zhang, Shun Gao, Feifei Ma, Jianpeng Dobre, Octavia A. 

作者机构：Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Tsinghua Univ Inst Artificial Intelligence Beijing 100084 Peoples R China Tsinghua Univ State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRis Dept Automat Beijing 100084 Peoples R China Mem Univ Fac Engn & Appl Sci Ece St John NF A1B 3X5 Canada 

出 版 物：《IEEE COMMUNICATIONS LETTERS》 (IEEE通讯快报)

年 卷 期：2020年第24卷第8期

页      面：1747-1751页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 

基　　金：National Key R&D Program of China [2017YFB1010002] National Natural Science Foundation of China [61931017, 61871455, 61901329] SAIC Science and Technology Foundation Natural Sciences and Engineering Research Council of Canada (NSERC) 

主　　题：Massive MIMO Channel estimation Correlation Artificial neural networks Feature extraction Decoding Massive multiple-input multiple-output graph channel tracking spatial correlation graph neural network 

摘      要：In this letter, 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.