Acquisition of channel state information for mmWave massive MIMO: traditional and machine learning-based approaches

被引:0
作者
Chenhao Qi
Peihao Dong
Wenyan Ma
Hua Zhang
Zaichen Zhang
Geoffrey Ye Li
机构
[1] Southeast University,School of Information Science and Engineering
[2] Nanjing University of Aeronautics and Astronautics,College of Electronic and Information Engineering
[3] Imperial College London,Department of Electrical and Electronic Engineering
来源
Science China Information Sciences | 2021年 / 64卷
关键词
beam training; channel estimation; machine learning; massive MIMO; millimeter wave (mmWave) communications;
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学科分类号
摘要
The accuracy of channel state information (CSI) acquisition directly affects the performance of millimeter wave (mmWave) communications. In this article, we provide an overview on CSI acquisition, including beam training and channel estimation for mmWave massive multiple-input multiple-output systems. The beam training can avoid the estimation of a high-dimension channel matrix, while the channel estimation can flexibly exploit advanced signal processing techniques. In addition to introducing the traditional and machine learning-based approaches in this article, we also compare different approaches in terms of spectral efficiency, computational complexity, and overhead.
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