Multi-Armed bandit beam alignment and tracking for mobile millimeter wave communications

Matthew B. Booth, Vinayak Suresh, Nicolo Michelusi, David J. Love

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

We propose a novel beam alignment and tracking algorithm for time-varying millimeter wave channels with a dynamic channel support. Millimeter wave beam alignment is challenging due to the expected large number of antennas. A multi-Armed bandit training beam selection policy is used to balance exploration of the set of feasible beams. We track the channel using a synthesis of sparse Bayesian learning and Kalman filtering and smoothing. Results show our algorithm has a more rapid rate of initial beam alignment compared to other beam selection policies and, for dynamic channel support, long-Term beamforming gain commensurate to omni-directional training.

Original languageEnglish (US)
Article number8723104
Pages (from-to)1244-1248
Number of pages5
JournalIEEE Communications Letters
Volume23
Issue number7
DOIs
StatePublished - Jul 2019
Externally publishedYes

Keywords

  • beam tracking
  • Kalman filter
  • Millimeter wave
  • multi-Armed bandits
  • sparse Bayesian learning

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

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