  Millimeter-wave (mmWave) communication systems rely on narrow beams for
achieving sufficient receive signal power. Adjusting these beams is typically
associated with large training overhead, which becomes particularly critical
for highly-mobile applications. Intuitively, since optimal beam selection can
benefit from the knowledge of the positions of communication terminals, there
has been increasing interest in leveraging position data to reduce the overhead
in mmWave beam prediction. Prior work, however, studied this problem using only
synthetic data that generally does not accurately represent real-world
measurements. In this paper, we investigate position-aided beam prediction
using a real-world large-scale dataset to derive insights into precisely how
much overhead can be saved in practice. Furthermore, we analyze which machine
learning algorithms perform best, what factors degrade inference performance in
real data, and which machine learning metrics are more meaningful in capturing
the actual communication system performance.
