Deep Learning Assisted mmWave Beam Prediction with Prior Low-frequency Information

Ke Ma, Dongxuan He, Hancun Sun, Zhaocheng Wang

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

9 Citations (Scopus)

Abstract

Huge overhead of beam training poses a significant challenge to mmWave communications. To address this issue, beam tracking has been widely investigated whereas existing methods are hard to handle serious multipath interference and non-stationary scenarios. Inspired by the spatial similarity between low-frequency and mmWave channels in non-standalone architectures, this paper proposes to utilize prior low-frequency information to predict the optimal mmWave beam, where deep learning is adopted to enhance the prediction accuracy. Specifically, periodically estimated low-frequency channel state information (CSI) is applied to track the movement of user equipment, and timing offset indicator is proposed to indicate the instant of mmWave beam training relative to low-frequency CSI estimation. Meanwhile, long-short term memory networks based dedicated models are designed to implement the prediction. Simulation results show that our proposed scheme can achieve higher beamforming gain than the conventional methods while requiring little overhead of mmWave beam training.

Original languageEnglish
Title of host publicationICC 2021 - IEEE International Conference on Communications, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728171227
DOIs
Publication statusPublished - Jun 2021
Externally publishedYes
Event2021 IEEE International Conference on Communications, ICC 2021 - Virtual, Online, Canada
Duration: 14 Jun 202123 Jun 2021

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607

Conference

Conference2021 IEEE International Conference on Communications, ICC 2021
Country/TerritoryCanada
CityVirtual, Online
Period14/06/2123/06/21

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