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Channel Compressed Estimation Based on k-Nearest Neighbor Learning

  • Hua-Feng Zhang
  • Chen-Guang He
  • Wen-Bin Zhang
  • Kuo Zhao
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 463)

Abstract

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.

Keywords

Hybrid analog/digital beamforming Channel estimation Millimeter wave MIMO Compressed sensing k-Nearest Neighbor learning 

Notes

Acknowledgments

This work was supported by the National Science and Technology Major Specific Projects of China (Grant No. 2015ZX03004002-004).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Harbin Institute of TechnologyHarbinChina
  2. 2.Communications Research CenterHarbin Institute of TechnologyHarbinChina

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