Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MmWave Beam Prediction with Situational Awareness: A Machine Learning
  Approach   Millimeter-wave communication is a challenge in the highly mobile vehicular
context. Traditional beam training is inadequate in satisfying low overheads
and latency. In this paper, we propose to combine machine learning tools and
situational awareness to learn the beam information (power, optimal beam index,
etc) from past observations. We consider forms of situational awareness that
are specific to the vehicular setting including the locations of the receiver
and the surrounding vehicles. We leverage regression models to predict the
received power with different beam power quantizations. The result shows that
situational awareness can largely improve the prediction accuracy and the model
can achieve throughput with little performance loss with almost zero overhead.

16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph