Fast Beamforming Strategy: Learning the AoD of the Dominant Path

Yongmin Song, Jeongwan Kang, Sunwoo Kim, Hyekyung Jwa, Jeehyeon Na

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Small cell networks and directional beamforming have been regarded as the solutions to achieve high data rates and compensate for the high path loss in the millimeter-wave (mmWave) communications. Large antenna array in mmWave communications causes considerable overhead to select the beam using exhaustive beam search, which significantly affects the efficiency of these mobile communications. In this paper, we propose a fast beamforming strategy by estimating the angle of departure (AoD) of the dominant path by exploiting the position of the mobile station, leveraging the deep neural network. Simulation results, based on accurate ray-tracing, show that we can achieve up to 0.58° of angle accuracy.

Original languageEnglish
Title of host publication2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages267-271
Number of pages5
ISBN (Electronic)9781728149851
DOIs
StatePublished - 2020 Feb
Event2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 - Fukuoka, Japan
Duration: 2020 Feb 192020 Feb 21

Publication series

Name2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

Conference

Conference2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
CountryJapan
CityFukuoka
Period20/02/1920/02/21

Keywords

  • AoD
  • beamforming
  • deep neural network
  • mm Wave

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    Song, Y., Kang, J., Kim, S., Jwa, H., & Na, J. (2020). Fast Beamforming Strategy: Learning the AoD of the Dominant Path. In 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 (pp. 267-271). [9065255] (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAIIC48513.2020.9065255