Kolmogorov Model for Large Millimeter-Wave Antenna Arrays: Learning-based Beam-Alignment

  • Wai Ming Chan
  • , Hadi Ghauch
  • , Taejoon Kim
  • , Elisabeth De Carvalho
  • , Gabor Fodor

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

Abstract

A new approach is presented to the problem of beam alignment for large-dimensional millimeter-wave antenna systems, with a single radio-frequency chain, based on the application of the Kolmogorov Model (KM) framework to enable learning-based beam alignment. Unlike the conventional exhaustive search-based approach, the proposed KM does not require the entire beam space search, i.e., the number of beam soundings can be extremely smaller than the conventional approach, which is achieved by exploiting the predictive power of KM. We show, across several metrics, that by just sounding 25% of the beams, the proposed method approaches the performance of the exhaustive search method. Simulation results that validate the training and test performance of KM and illustrate the new method with significantly reduced overhead are presented.

Original languageEnglish
Title of host publicationConference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages411-415
Number of pages5
ISBN (Electronic)9781728143002
DOIs
Publication statusPublished - 1 Nov 2019
Event53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States
Duration: 3 Nov 20196 Nov 2019

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2019-November
ISSN (Print)1058-6393

Conference

Conference53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
Country/TerritoryUnited States
CityPacific Grove
Period3/11/196/11/19

Keywords

  • Kolmogorov model
  • Millimeter wave
  • beam-alignment
  • learning-based beam alignment
  • prediction

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