  Scaling the number of antennas up is a key characteristic of current and
future wireless communication systems. The hardware cost and power consumption,
however, motivate large-scale MIMO systems, especially at millimeter wave
(mmWave) bands, to rely on analog-only or hybrid analog/digital transceiver
architectures. With these architectures, mmWave base stations normally use
pre-defined beamforming codebooks for both initial access and data
transmissions. Current beam codebooks, however, generally adopt single-lobe
narrow beams and scan the entire angular space. This leads to high beam
training overhead and loss in the achievable beamforming gains. In this paper,
we propose a new machine learning framework for learning beamforming codebooks
in hardware-constrained large-scale MIMO systems. More specifically, we develop
a neural network architecture that accounts for the hardware constraints and
learns beam codebooks that adapt to the surrounding environment and the user
locations. Simulation results highlight the capability of the proposed solution
in learning multi-lobe beams and reducing the codebook size, which leads to
noticeable gains compared to classical codebook design approaches.
