  The widespread proliferation of mmW devices has led to a surge of interest in
antenna arrays. This interest in arrays is due to their ability to steer beams
in desired directions, for the purpose of increasing signal-power and/or
decreasing interference levels. To enable beamforming, array coefficients are
typically stored in look-up tables (LUTs) for subsequent referencing. While
LUTs enable fast sweep times, their limited memory size restricts the number of
beams the array can produce. Consequently, a receiver is likely to be offset
from the main beam, thus decreasing received power, and resulting in
sub-optimal performance. In this letter, we present BeamShaper, a deep neural
network (DNN) framework, which enables fast and accurate beamsteering in any
desirable 3-D direction. Unlike traditional finite-memory LUTs which support a
fixed set of beams, BeamShaper utilizes a trained NN model to generate the
array coefficients for arbitrary directions in \textit{real-time}. Our
simulations show that BeamShaper outperforms contemporary LUT based solutions
in terms of cosine-similarity and central angle in time scales that are
slightly higher than LUT based solutions. Additionally, we show that our DNN
based approach has the added advantage of being more resilient to the effects
of quantization noise generated while using digital phase-shifters.
