Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generative Machine Learning for Robust Free-Space Communication   Realistic free-space optical communications systems suffer from turbulent
propagation of light through the atmosphere and detector noise at the receiver,
which can significantly degrade the optical mode quality of the received state,
increase cross-talk between modes, and correspondingly increase the symbol
error ratio (SER) of the system. In order to overcome these obstacles, we
develop a state-of-the-art generative machine learning (GML) and convolutional
neural network (CNN) system in combination, and demonstrate its efficacy in a
free-space optical (FSO) communications setting. The system corrects for the
distortion effects due to turbulence and reduces detector noise, resulting in
significantly lowered SERs and cross-talk at the output of the receiver, while
requiring no feedback. This scheme is straightforward to scale, and may provide
a concrete and cost effective technique to establishing long range classical
and quantum communication links in the near future.

5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat