  Utilizing millimeter-wave (mmWave) frequencies for wireless communication in
\emph{mobile} systems is challenging since it requires continuous tracking of
the beam direction. Recently, beam tracking techniques based on channel
sparsity and/or Kalman filter-based techniques were proposed where the
solutions use assumptions regarding the environment and device mobility that
may not hold in practical scenarios. In this paper, we explore a machine
learning-based approach to track the angle of arrival (AoA) for specific paths
in realistic scenarios. In particular, we use a recurrent neural network (R-NN)
structure with a modified cost function to track the AoA. We propose methods to
train the network in sequential data, and study the performance of our proposed
solution in comparison to an extended Kalman filter based solution in a
realistic mmWave scenario based on stochastic channel model from the QuaDRiGa
framework. Results show that our proposed solution outperforms an extended
Kalman filter-based method by reducing the AoA outage probability, and thus
reducing the need for frequent beam search.
