  Fast and precise beam alignment is crucial for high-quality data transmission
in millimeter-wave (mmWave) communication systems, where large-scale antenna
arrays are utilized to overcome the severe propagation loss. To tackle the
challenging problem, we propose a novel deep learning-based hierarchical beam
alignment method for both multiple-input single-output (MISO) and
multiple-input multiple-output (MIMO) systems, which learns two tiers of
probing codebooks (PCs) and uses their measurements to predict the optimal beam
in a coarse-to-fine search manner. Specifically, a hierarchical beam alignment
network (HBAN) is developed for MISO systems, which first performs coarse
channel measurement using a 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 propounded HBAN is trained in two steps: the tier-1
PC and the tier-2 PC selector are first trained jointly, followed by the joint
training of all the tier-2 PCs and beam predictors. Furthermore, an HBAN for
MIMO systems is proposed to directly predict the optimal beam pair without
performing beam alignment individually at the transmitter and receiver.
Numerical results demonstrate that the proposed HBANs are superior to the
state-of-art methods in both alignment accuracy and signaling overhead
reduction.
