Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning of Time-Frequency Attention Mechanism for Automatic Modulation
  Recognition   Recent learning-based image classification and speech recognition approaches
make extensive use of attention mechanisms to achieve state-of-the-art
recognition power, which demonstrates the effectiveness of attention
mechanisms. Motivated by the fact that the frequency and time information of
modulated radio signals are crucial for modulation mode recognition, this paper
proposes a time-frequency attention mechanism for a convolutional neural
network (CNN)-based modulation recognition framework. The proposed
time-frequency attention module is designed to learn which channel, frequency
and time information is more meaningful in CNN for modulation recognition. We
analyze the effectiveness of the proposed time-frequency attention mechanism
and compare the proposed method with two existing learning-based methods.
Experiments on an open-source modulation recognition dataset show that the
recognition performance of the proposed framework is better than those of the
framework without time-frequency attention and existing learning-based methods.

23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex