Multi-Armed Bandit Beam Alignment and Tracking for Mobile Millimeter Wave Communications

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 explor...

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Bibliographic Details
Published inIEEE communications letters Vol. 23; no. 7; pp. 1244 - 1248
Main Authors Booth, Matthew B., Suresh, Vinayak, Michelusi, Nicolo, Love, David J.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.07.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Alignment
Array signal processing
Bayes methods
Bayesian analysis
beam tracking
Beamforming
Beams (radiation)
Channel estimation
Kalman filter
Kalman filters
Machine learning
Millimeter wave
Millimeter waves
Mobile communication systems
Multi-armed bandit problems
multi-armed bandits
Receivers
sparse Bayesian learning
Tracking
Training
Transmitters
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Summary: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.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2019.2919016