Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning in Physical Layer Communications   Deep learning (DL) has shown the great potentials to break the bottleneck of
communication systems. This article provides an overview on the recent
advancements in DL-based physical layer communications. DL can improve the
performance of each individual block in communication systems or optimize the
whole transmitter/receiver. Therefore, we categorize the applications of DL in
physical layer communications into systems with and without block structures.
For DL-based communication systems with block structures, we demonstrate the
power of DL in signal compression and signal detection. We also discuss the
recent endeavors in developing end-to-end communication systems. Finally, the
potential research directions are identified to boost the intelligent physical
layer communications with DL.

11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex