  In this paper, we propose a deep learning-based beam tracking method for
millimeter-wave (mmWave)communications. Beam tracking is employed for
transmitting the known symbols using the sounding beams and tracking
time-varying channels to maintain a reliable communication link. When the pose
of a user equipment (UE) device varies rapidly, the mmWave channels also tend
to vary fast, which hinders seamless communication. Thus, models that can
capture temporal behavior of mmWave channels caused by the motion of the device
are required, to cope with this problem. Accordingly, we employa deep neural
network to analyze the temporal structure and patterns underlying in the
time-varying channels and the signals acquired by inertial sensors. We propose
a model based on long short termmemory (LSTM) that predicts the distribution of
the future channel behavior based on a sequence of input signals available at
the UE. This channel distribution is used to 1) control the sounding beams
adaptively for the future channel state and 2) update the channel estimate
through the measurement update step under a sequential Bayesian estimation
framework. Our experimental results demonstrate that the proposed method
achieves a significant performance gain over the conventional beam tracking
methods under various mobility scenarios.
