  Millimeter-wave cellular communication requires beamforming procedures that
enable alignment of the transmitter and receiver beams as the user equipment
(UE) moves. For efficient beam tracking it is advantageous to classify users
according to their traffic and mobility patterns. Research to date has
demonstrated efficient ways of machine learning based UE classification.
Although different machine learning approaches have shown success, most of them
are based on physical layer attributes of the received signal. This, however,
imposes additional complexity and requires access to those lower layer signals.
In this paper, we show that traditional supervised and even unsupervised
machine learning methods can successfully be applied on higher layer channel
measurement reports in order to perform UE classification, thereby reducing the
complexity of the classification process.
