Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning-Based UE Classification in Millimeter-Wave Cellular Systems
  With Mobility   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.

18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom