典型文献
Deep learning for fast channel estimation in millimeter-wave MIMO systems
文献摘要：
Channel estimation has been considered as a key issue in the millimeter-wave (mmWave) massive multi-input multi-output (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 infor-mation (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 accu-racy compared with existing schemes.
文献关键词：
中图分类号：
作者姓名：
LYU Siting;LI Xiaohui;FAN Tao;LIU Jiawen;SHI Mingli
作者机构：
School of Telecommunication Engineering,Xidian University,Xi'an 710071,China;State Key Laboratory of Integrated Service Networks,Xidian University,Xi'an 710071,China
文献出处：
引用格式：
[1]LYU Siting;LI Xiaohui;FAN Tao;LIU Jiawen;SHI Mingli-.Deep learning for fast channel estimation in millimeter-wave MIMO systems)[J].系统工程与电子技术（英文版）,2022(06):1088-1095
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B类：
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AB值：
0.555337
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