  Millimeter wave vehicular channels exhibit structure that can be exploited
for beam alignment with fewer channel measurements compared to exhaustive beam
search. With fixed layouts of roadside buildings and regular vehicular moving
trajectory, the dominant path directions of channels will likely be among a
subset of beam directions instead of distributing randomly over the whole
beamspace. In this paper, we propose a deep learning-based technique to design
a structured compressed sensing (CS) matrix that is well suited to the
underlying channel distribution for mmWave vehicular beam alignment. The
proposed approach leverages both sparsity and the particular spatial structure
that appears in vehicular channels. We model the compressive channel
acquisition by a two-dimensional (2D) convolutional layer followed by dropout.
We design fully-connected layers to optimize channel acquisition and beam
alignment. We incorporate the low-resolution phase shifter constraint during
neural network training by using projected gradient descent for weight updates.
Furthermore, we exploit channel spectral structure to optimize the power
allocated for different subcarriers. Simulations indicate that our deep
learning-based approach achieves better beam alignment than standard CS
techniques which use random phase shift-based design. Numerical experiments
also show that one single subcarrier is sufficient to provide necessary
information for beam alignment.
