Bayesian tree search for beamforming training in millimeter wave wireless communication systems

Wei Chen Chen, Hsiao Ting Chiu, Rung-Hung Gau

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

4 Scopus citations

Abstract

In this paper, we propose novel algorithms of beam-forming training for millimeter wave wireless communication systems. Instead of searching the whole codebook organized as a binary tree, we propose using Bayesian tree search algorithms to reduce the average delay of beamforming training. In addition, we design algorithms that derive an optimal threshold for the proposed opportunistic one-threshold tree search algorithm and an optimal pair of thresholds for the proposed opportunistic two-threshold tree search algorithm. Furthermore, we propose using quantization to efficiently calculate the likelihood ratio in the proposed opportunistic tree search algorithms. Our simulation results show that the proposed algorithms could significantly reduce the average delay of beamforming training.

Original languageEnglish
Title of host publication2018 IEEE Wireless Communications and Networking Conference, WCNC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538617342
DOIs
StatePublished - 8 Jun 2018
Event2018 IEEE Wireless Communications and Networking Conference, WCNC 2018 - Barcelona, Spain
Duration: 15 Apr 201818 Apr 2018

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
Volume2018-April
ISSN (Print)1525-3511

Conference

Conference2018 IEEE Wireless Communications and Networking Conference, WCNC 2018
Country/TerritorySpain
CityBarcelona
Period15/04/1818/04/18

Keywords

  • Beam-forming training
  • Millimeter wave wireless communication
  • Statistical detection theory
  • Tree search

Fingerprint

Dive into the research topics of 'Bayesian tree search for beamforming training in millimeter wave wireless communication systems'. Together they form a unique fingerprint.

Cite this