Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Select for MIMO Radar based on Hybrid Analog-Digital
  Beamforming   In this paper, we propose an energy-efficient radar beampattern design
framework for a Millimeter Wave (mmWave) massive multi-input multi-output
(mMIMO) system, equipped with a hybrid analog-digital (HAD) beamforming
structure. Aiming to reduce the power consumption and hardware cost of the
mMIMO system, we employ a machine learning approach to synthesize the probing
beampattern based on a small number of RF chains and antennas. By leveraging a
combination of softmax neural networks, the proposed solution is able to
achieve a desirable beampattern with high accuracy.

2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn