Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Source Coding Based Millimeter-Wave Channel Estimation with Deep
  Learning Based Decoding   The speed at which millimeter-Wave (mmWave) channel estimation can be carried
out is critical for the adoption of mmWave technologies. This is particularly
crucial because mmWave transceivers are equipped with large antenna arrays to
combat severe path losses, which consequently creates large channel matrices,
whose estimation may incur significant overhead. This paper focuses on the
mmWave channel estimation problem. Our objective is to reduce the number of
measurements required to reliably estimate the channel. Specifically, channel
estimation is posed as a "source compression" problem in which measurements
mimic an encoded (compressed) version of the channel. Decoding the observed
measurements, a task which is traditionally computationally intensive, is
performed using a deep-learning-based approach, facilitating a high-performance
channel discovery. Our solution not only outperforms state-of-the-art
compressed sensing methods, but it also determines the lower bound on the
number of measurements required for reliable channel discovery.

31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc