Channel Compressed Estimation Based on k-Nearest Neighbor Learning

被引：0
|
作者：
Zhang, Hua-Feng [1 ]
He, Chen-Guang [2 ]
Zhang, Wen-Bin [1 ,2 ]
Zhao, Kuo [1 ,2 ]
机构：
[1] Harbin Inst Technol, Xidazhijie 92, Harbin 150006, Heilongjiang, Peoples R China
[2] Harbin Inst Technol, Commun Res Ctr, Harbin, Heilongjiang, Peoples R China
来源：
COMMUNICATIONS, SIGNAL PROCESSING, AND SYSTEMS | 2019年 / 463卷
关键词：
Hybrid analog/digital beamforming; Channel estimation; Millimeter wave; MIMO; Compressed sensing; k-Nearest Neighbor learning;
D O I：
10.1007/978-981-10-6571-2_123
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
MmWave communication is receiving tremendous interest by academia, industry, and government for 5G cellular systems. Due to the short wavelength, the millimeter wave experiences high path loss and penetration loss. Compensating for path loss will require beamforming, which is based on channel estimation. However, in the actual environment, the number of multi-path is unknown. In order to solve the problem in millimeter wave system, this paper estimates the number of multi-path by utilizing k-Nearest Neighbor learning. Then we use the OMP algorithm to estimate the channel. The simulations show that the k-Nearest Neighbor learning can get better performance of channel estimations in the mmWave MIMO communication.
引用
收藏
页码：1018 / 1024
页数：7
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