Beam Training in mmWave Vehicular Systems: Machine Learning for
  Decoupling Beam Selection
Ibrahim Kilinc Ryan M. Dreifuerst Junghoon Kim Robert W. Heath Jr
Codebook-based beam selection is one approach for configuring millimeter wave
communication links. The overhead required to reconfigure the transmit and
receive beam pair, though, increases in highly dynamic vehicular communication
systems. Location information coupled with machine learning (ML) beam
recommendation is one way to reduce the overhead of beam pair selection. In
this paper, we develop ML-based location-aided approaches to decouple the beam
selection between the user equipment (UE) and the base station (BS). We
quantify the performance gaps due to decoupling beam selection and also
disaggregating the UE's location information from the BS. Our simulation
results show that decoupling beam selection with available location information
at the BS performs comparable to joint beam pair selection at the BS. Moreover,
decoupled beam selection without location closely approaches the performance of
beam pair selection at the BS when sufficient beam pairs are swept.