Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning-Based Quantization of L-Values for Gray-Coded Modulation   In this work, a deep learning-based quantization scheme for log-likelihood
ratio (L-value) storage is introduced. We analyze the dependency between the
average magnitude of different L-values from the same quadrature amplitude
modulation (QAM) symbol and show they follow a consistent ordering. Based on
this we design a deep autoencoder that jointly compresses and separately
reconstructs each L-value, allowing the use of a weighted loss function that
aims to more accurately reconstructs low magnitude inputs. Our method is shown
to be competitive with state-of-the-art maximum mutual information quantization
schemes, reducing the required memory footprint by a ratio of up to two and a
loss of performance smaller than 0.1 dB with less than two effective bits per
L-value or smaller than 0.04 dB with 2.25 effective bits. We experimentally
show that our proposed method is a universal compression scheme in the sense
that after training on an LDPC-coded Rayleigh fading scenario we can reuse the
same network without further training on other channel models and codes while
preserving the same performance benefits.

8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph