Efficient beamforming training and channel estimation for MMWave MIMO-OFDM systems

Hanyu Wang, Jun Fang, Huiping Duan, Hongbin Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

We consider the problem of channel estimation for millimeter wave (mmWave) MIMO-OFDM systems. To efficiently probe the channel, the transmitter forms multiple beams simultaneously and steer them towards different directions. The objective of this paper is to devise the beamtraining patterns and develop an efficient algorithm to estimate the channel. By exploiting the common sparsity inherent in MIMO-OFDM mmWave channels, we develop a sparse bipartite graph coding-based method for joint beamforming training and channel estimation. Simulation results are provided to show the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop, SAM 2020
ISBN (Electronic)9781728119465
DOIs
StatePublished - Jun 2020
Event11th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2020 - Hangzhou, China
Duration: 8 Jun 202011 Jun 2020

Publication series

NameProceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
Volume2020-June
ISSN (Electronic)2151-870X

Conference

Conference11th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2020
Country/TerritoryChina
CityHangzhou
Period8/06/2011/06/20

Keywords

  • Beam alignment
  • MIMO-OFDM systems
  • MmWave communication

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