  Beam alignment - the process of finding an optimal directional beam pair - is
a challenging procedure crucial to millimeter wave (mmWave) communication
systems. We propose a novel beam alignment method that learns a site-specific
probing codebook and uses the probing codebook measurements to predict the
optimal narrow beam. An end-to-end neural network (NN) architecture is designed
to jointly learn the probing codebook and the beam predictor. The learned
codebook consists of site-specific probing beams that can capture particular
characteristics of the propagation environment. The proposed method relies on
beam sweeping of the learned probing codebook, does not require additional
context information, and is compatible with the beam sweeping-based beam
alignment framework in 5G. Using realistic ray-tracing datasets, we demonstrate
that the proposed method can achieve high beam alignment accuracy and
signal-to-noise ratio (SNR) while significantly - by roughly a factor of 3 in
our setting - reducing the beam sweeping complexity and latency.
