  Supporting high mobility in millimeter wave (mmWave) systems enables a wide
range of important applications such as vehicular communications and wireless
virtual/augmented reality. Realizing this in practice, though, requires
overcoming several challenges. First, the use of narrow beams and the
sensitivity of mmWave signals to blockage greatly impact the coverage and
reliability of highly-mobile links. Second, highly-mobile users in dense mmWave
deployments need to frequently hand-off between base stations (BSs), which is
associated with critical control and latency overhead. Further, identifying the
optimal beamforming vectors in large antenna array mmWave systems requires
considerable training overhead, which significantly affects the efficiency of
these mobile systems. In this paper, a novel integrated machine learning and
coordinated beamforming solution is developed to overcome these challenges and
enable highly-mobile mmWave applications. In the proposed solution, a number of
distributed yet coordinating BSs simultaneously serve a mobile user. This user
ideally needs to transmit only one uplink training pilot sequence that will be
jointly received at the coordinating BSs using omni or quasi-omni beam
patterns. These received signals draw a defining signature not only for the
user location, but also for its interaction with the surrounding environment.
The developed solution then leverages a deep learning model that learns how to
use these signatures to predict the beamforming vectors at the BSs. This
renders a comprehensive solution that supports highly-mobile mmWave
applications with reliable coverage, low latency, and negligible training
overhead. Simulation results show that the proposed deep-learning coordinated
beamforming strategy approaches the achievable rate of the genie-aided solution
that knows the optimal beamforming vectors with no training overhead.
