  The problem of beam alignment and tracking in high mobility scenarios such as
high-speed railway (HSR) becomes extremely challenging, since large overhead
cost and significant time delay are introduced for fast time-varying channel
estimation. To tackle this challenge, we propose a learning-aided beam
prediction scheme for HSR networks, which predicts the beam directions and the
channel amplitudes within a period of future time with fine time granularity,
using a group of observations. Concretely, we transform the problem of
high-dimensional beam prediction into a two-stage task, i.e., a low-dimensional
parameter estimation and a cascaded hybrid beamforming operation. In the first
stage, the location and speed of a certain terminal are estimated by maximum
likelihood criterion, and a data-driven data fusion module is designed to
improve the final estimation accuracy and robustness. Then, the probable future
beam directions and channel amplitudes are predicted, based on the HSR scenario
priors including deterministic trajectory, motion model, and channel model.
Furthermore, we incorporate a learnable non-linear mapping module into the
overall beam prediction to allow non-linear tracks. Both of the proposed
learnable modules are model-based and have a good interpretability. Compared to
the existing beam management scheme, the proposed beam prediction has (near)
zero overhead cost and time delay. Simulation results verify the effectiveness
of the proposed scheme.
