Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AutoEncoders for Training Compact Deep Learning RF Classifiers for
  Wireless Protocols   We show that compact fully connected (FC) deep learning networks trained to
classify wireless protocols using a hierarchy of multiple denoising
autoencoders (AEs) outperform reference FC networks trained in a typical way,
i.e., with a stochastic gradient based optimization of a given FC architecture.
Not only is the complexity of such FC network, measured in number of trainable
parameters and scalar multiplications, much lower than the reference FC and
residual models, its accuracy also outperforms both models for nearly all
tested SNR values (0 dB to 50dB). Such AE-trained networks are suited for
in-situ protocol inference performed by simple mobile devices based on noisy
signal measurements. Training is based on the data transmitted by real devices,
and collected in a controlled environment, and systematically augmented by a
policy-based data synthesis process by adding to the signal any subset of
impairments commonly seen in a wireless receiver.

29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat