  Beamforming-capable antenna arrays overcome the high free-space path loss at
higher carrier frequencies. However, the beams must be properly aligned to
ensure that the highest power is radiated towards (and received by) the user
equipment (UE). While there are methods that improve upon an exhaustive search
for optimal beams by some form of hierarchical search, they can be prone to
return only locally optimal solutions with small beam gains. Other approaches
address this problem by exploiting contextual information, e.g., the position
of the UE or information from neighboring base stations (BS), but the burden of
computing and communicating this additional information can be high. Methods
based on machine learning so far suffer from the accompanying training,
performance monitoring and deployment complexity that hinders their application
at scale.
  This paper proposes a novel method for solving the initial beam-discovery
problem. It is scalable, and easy to tune and to implement. Our algorithm is
based on a recommender system that associates groups (i.e., UEs) and
preferences (i.e., beams from a codebook) based on a training data set.
Whenever a new UE needs to be served our algorithm returns the best beams in
this user cluster. Our simulation results demonstrate the efficiency and
robustness of our approach, not only in single BS setups but also in setups
that require a coordination among several BSs. Our method consistently
outperforms standard baseline algorithms in the given task.
