Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: "Machine LLRning": Learning to Softly Demodulate   Soft demodulation, or demapping, of received symbols back into their conveyed
soft bits, or bit log-likelihood ratios (LLRs), is at the very heart of any
modern receiver. In this paper, a trainable universal neural network-based
demodulator architecture, dubbed "LLRnet", is introduced. LLRnet facilitates an
improved performance with significantly reduced overall computational
complexity. For instance for the commonly used quadrature amplitude modulation
(QAM), LLRnet demonstrates LLR estimates approaching the optimal log maximum
a-posteriori inference with an order of magnitude less operations than that of
the straightforward exact implementation. Link-level simulation examples for
the application of LLRnet to 5G-NR and DVB-S.2 are provided. LLRnet is a (yet
another) powerful example for the usefulness of applying machine learning to
physical layer design.

3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org