  Fast and precise beam alignment is crucial to support high-quality data
transmission in millimeter wave (mmWave) communication systems. In this work,
we propose a novel deep learning based hierarchical beam alignment method that
learns two tiers of probing codebooks (PCs) and uses their measurements to
predict the optimal beam in a coarse-to-fine searching manner. Specifically,
the proposed method first performs coarse channel measurement using the tier-1
PC, then selects a tier-2 PC for fine channel measurement, and finally predicts
the optimal beam based on both coarse and fine measurements. The proposed deep
neural network (DNN) architecture is trained in two steps. First, the tier-1 PC
and the tier-2 PC selector are trained jointly. After that, all the tier-2 PCs
together with the optimal beam predictors are trained jointly. The learned
hierarchical PCs can capture the features of propagation environment. Numerical
results based on realistic ray-tracing datasets demonstrate that the proposed
method is superior to the state-of-art beam alignment methods in both alignment
accuracy and sweeping overhead.
