  To establish reliable and long-range millimeter-wave (mmWave) communication,
beamforming is deemed to be a promising solution. Although beamforming can be
done in the digital and analog domains, both approaches are hindered by several
constraints when it comes to mmWave communications. For example, performing
fully digital beamforming in mmWave systems involves using many radio frequency
(RF) chains, which are expensive and consume high power. This necessitates
finding more efficient ways for using fewer RF chains while taking advantage of
the large antenna arrays. One way to overcome this challenge is to employ
(partially or fully) analog beamforming through proper configuration of
phase-shifters. Existing works on mmWave analog beam design either rely on the
knowledge of the channel state information (CSI) per antenna within the array,
require a large search time (e.g., exhaustive search) or do not guarantee a
minimum beamforming gain (e.g., codebook based beamforming). In this paper, we
propose a beam design technique that reduces the search time and does not
require CSI while guaranteeing a minimum beamforming gain. The key idea derives
from observations drawn from real-life measurements. It was observed that for a
given propagation environment (e.g., coverage area of a mmWave BS) the
azimuthal angles of dominant signals could be more probable from certain angles
than others. Thus, pre-collected measurements could used to build a beamforming
codebook that regroups the most probable beam designs. We invoke Bayesian
learning for measurements clustering. We evaluate the efficacy of the proposed
scheme in terms of building the codebook and assessing its performance through
real-life measurements. We demonstrate that the training time required by the
proposed scheme is only 5% of that of exhaustive search. This crucial gain is
obtained while achieving a minimum targeted beamforming gain.
