  Base station (BS) placement in mobile networks is critical to the efficient
use of resources in any communication system and one of the main factors that
determines the quality of communication. Although there is ample literature on
the optimum placement of BSs for sub-6 GHz bands, channel propagation
characteristics, such as penetration loss, are notably different in
millimeter-wave (mmWave) bands than in sub-6 GHz bands. Therefore, designated
solutions are needed for mmWave systems to have reliable quality of service
(QoS) assessment. This article proposes a multi-armed bandit (MAB) learning
approach for the mmWave BS placement problem. The proposed solution performs
viewshed analysis to identify the areas that are visible to a given BS location
by considering the 3D geometry of the outdoor environments. Coverage
probability, which is used as the QoS metric, is calculated using the
appropriate path loss model depending on the viewshed analysis and a
probabilistic blockage model and then fed to the MAB learning mechanism. The
optimum BS location is then determined based on the expected reward that the
candidate locations attain at the end of the training process. Unlike the
optimization-based techniques, this method can capture the time-varying
behavior of the channel and find the optimal BS locations that maximize
long-term performance.
