Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Wideband and Entropy-Aware Deep Soft Bit Quantization   Deep learning has been recently applied to physical layer processing in
digital communication systems in order to improve end-to-end performance. In
this work, we introduce a novel deep learning solution for soft bit
quantization across wideband channels. Our method is trained end-to-end with
quantization- and entropy-aware augmentations to the loss function and is used
at inference in conjunction with source coding to achieve near-optimal
compression gains over wideband channels. To efficiently train our method, we
prove and verify that a fixed feature space quantization scheme is sufficient
for efficient learning. When tested on channel distributions never seen during
training, the proposed method achieves a compression gain of up to $10 \%$ in
the high SNR regime versus previous state-of-the-art methods. To encourage
reproducible research, our implementation is publicly available at
https://github.com/utcsilab/wideband-llr-deep.

13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph