  Huge overhead of beam training poses a significant challenge to mmWave
communications. To address this issue, beam tracking has been widely
investigated whereas existing methods are hard to handle serious multipath
interference and non-stationary scenarios. Inspired by the spatial similarity
between low-frequency and mmWave channels in non-standalone architectures, this
paper proposes to utilize prior low-frequency information to predict the
optimal mmWave beam, where deep learning is adopted to enhance the prediction
accuracy. Specifically, periodically estimated low-frequency channel state
information (CSI) is applied to track the movement of user equipment, and
timing offset indicator is proposed to indicate the instant of mmWave beam
training relative to low-frequency CSI estimation. Meanwhile, long-short term
memory networks based dedicated models are designed to implement the
prediction. Simulation results show that our proposed scheme can achieve higher
beamforming gain than the conventional methods while requiring little overhead
of mmWave beam training.
