Deep learning for fast channel estimation in millimeter-wave MIMO systems  

作　　者：LYU Siting LI Xiaohui FAN Tao LIU Jiawen SHI Mingli 

机构地区：[1]School of Telecommunication Engineering,Xidian University,Xi’an 710071,China [2]State Key Laboratory of Integrated Service Networks,Xidian University,Xi’an 710071,China

出　　处：《Journal of Systems Engineering and Electronics》2022年第6期1088-1095,共8页系统工程与电子技术（英文版）

基　　金：supported by the National Key R&D Program of China(2018YFB1802004);111 Project(B08038)。

摘　　要：Channel estimation has been considered as a key issue in the millimeter-wave(mmWave)massive multi-input multioutput(MIMO)communication systems,which becomes more challenging with a large number of antennas.In this paper,we propose a deep learning(DL)-based fast channel estimation method for mmWave massive MIMO systems.The proposed method can directly and effectively estimate channel state information(CSI)from received data without performing pilot signals estimate in advance,which simplifies the estimation process.Specifically,we develop a convolutional neural network(CNN)-based channel estimation network for the case of dimensional mismatch of input and output data,subsequently denoted as channel(H)neural network(HNN).It can quickly estimate the channel information by learning the inherent characteristics of the received data and the relationship between the received data and the channel,while the dimension of the received data is much smaller than the channel matrix.Simulation results show that the proposed HNN can gain better channel estimation accuracy compared with existing schemes.

关 键 词：millimeter-wave(mmWave) channel estimation deep learning(DL) dimensional mismatch channel state information(CSI) 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TN929.5[自动化与计算机技术—控制科学与工程]