Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning for Beam-Management: State-of-the-Art, Opportunities and
  Challenges   Benefiting from huge bandwidth resources, millimeter-wave (mmWave)
communications provide one of the most promising technologies for
next-generation wireless networks. To compensate for the high pathloss of
mmWave signals, large-scale antenna arrays are required both at the base
stations and user equipment to establish directional beamforming, where
beam-management is adopted to acquire and track the optimal beam pair having
the maximum received power. Naturally, narrow beams are required for achieving
high beamforming gain, but they impose enormous training overhead and high
sensitivity to blockages. As a remedy, deep learning (DL) may be harnessed for
beam-management. First, the current state-of-the-art is reviewed, followed by
the associated challenges and future research opportunities. We conclude by
highlighting the associated DL design insights and novel beam-management
mechanisms.

34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin