Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TurboNet: A Model-driven DNN Decoder Based on Max-Log-MAP Algorithm for
  Turbo Code   This paper presents TurboNet, a novel model-driven deep learning (DL)
architecture for turbo decoding that combines DL with the traditional
max-log-maximum a posteriori (MAP) algorithm. To design TurboNet, we unfold the
original iterative structure for turbo decoding and replace each iteration by a
deep neural network (DNN) decoding unit. In particular, the DNN decoding unit
is obtained by parameterizing the max-log-MAP algorithm rather than replace the
whole decoder with a black box fully connected DNN architecture. With the
proposed architecture, the parameters can be efficiently learned from training
data, and thus TurboNet learns to appropriately use systematic and parity
information to offer higher error correction capabilities and decrease
computational complexity compared with existing methods. Furthermore,
simulation results prove TurboNet's superiority in signal-to-noise ratio
generalizations.

30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph