Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems

被引:545
作者
He, Hengtao [1 ]
Wen, Chao-Kai [2 ]
Jin, Shi [1 ]
Li, Geoffrey Ye [3 ]
机构
[1] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Jiangsu, Peoples R China
[2] Natl Sun Yat Sen Univ, Inst Commun Engn, Kaohsiung 804, Taiwan
[3] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA
基金
美国国家科学基金会;
关键词
Millimeter wave; beamspace MIMO; channel estimation; deep learning; neural network;
D O I
10.1109/LWC.2018.2832128
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Channel estimation is very challenging when the receiver is equipped with a limited number of radio-frequency 9RF) chains in beamspace millimeter-wave massive multipleinput and multiple-output systems. To solve this problem, we exploit a learned denoising-based approximate message passing 9LDAMP) network. This neural network can learn channel structure and estimate channel from a large number of training data. Furthermore, we provide an analytical framework on the asymptotic performance of the channel estimator. Based on our analysis and simulation results, the LDAMP neural network significantly outperforms state-of-the-art compressed sensing-based algorithms even when the receiver is equipped with a small number of RF chains.
引用
收藏
页码:852 / 855
页数:4
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