  Due to the very narrow beam used in millimeter wave communication (mmWave),
beam alignment (BA) is a critical issue. In this work, we investigate the issue
of mmWave BA and present a novel beam alignment scheme on the basis of a
machine learning strategy, Bayesian optimization (BO). In this context, we
consider the beam alignment issue to be a black box function and then use BO to
find the possible optimal beam pair. During the BA procedure, this strategy
exploits information from the measured beam pairs to predict the best beam
pair. In addition, we suggest a novel BO algorithm based on the gradient
boosting regression tree model. The simulation results demonstrate the spectral
efficiency performance of our proposed schemes for BA using three different
surrogate models. They also demonstrate that the proposed schemes can achieve
spectral efficiency with a small overhead when compared to the orthogonal match
pursuit (OMP) algorithm and the Thompson sampling-based multi-armed bandit
(TS-MAB) method.
