Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeepIC: Coding for Interference Channels via Deep Learning   The two-user interference channel is a model for multi one-to-one
communications, where two transmitters wish to communicate with their
corresponding receivers via a shared wireless medium. Two most common and
simple coding schemes are time division (TD) and treating interference as noise
(TIN). Interestingly, it is shown that there exists an asymptotic scheme,
called Han-Kobayashi scheme, that performs better than TD and TIN. However,
Han-Kobayashi scheme has impractically high complexity and is designed for
asymptotic settings, which leads to a gap between information theory and
practice.
  In this paper, we focus on designing practical codes for interference
channels. As it is challenging to analytically design practical codes with
feasible complexity, we apply deep learning to learn codes for interference
channels. We demonstrate that DeepIC, a convolutional neural network-based code
with an iterative decoder, outperforms TD and TIN by a significant margin for
two-user additive white Gaussian noise channels with moderate amount of
interference.

32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th