Citation: Yan, X.; Zhang, Y.; Rao, X.;
Wang, Q.; Wu, H.-C.; Wu, Y. Novel
Cooperative Automatic Modulation
Classification Using Vectorized Soft
Decision Fusion for Wireless Sensor
Networks. Sensors 2022, 22, 1797.
https://doi.org/10.3390/s22051797
Academic Editors: Alvaro Araujo
Pinto and Hacene Fouchal
Received: 31 December 2021
Accepted: 21 February 2022
Published: 24 February 2022
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Communication
Novel Cooperative Automatic Modulation Classification Using
Vectorized Soft Decision Fusion for Wireless Sensor Networks
Xiao Yan
1
, Yan Zhang
1
, Xiaoxue Rao
1
, Qian Wang
1,
* , Hsiao-Chun Wu
2
and Yiyan Wu
3
1
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China,
Chengdu 611731, China; yanxiao@uestc.edu.cn (X.Y.); zhangryan@std.uestc.edu.cn (Y.Z.);
raoxiaoxue@std.uestc.edu.cn (X.R.)
2
School of Electrical Engineering and Computer Science, Louisiana State University,
Baton Rouge, LA 70803, USA; wu@ece.lsu.edu
3
Communications Research Centre, Ottawa, ON K2H 8S2, Canada; yiyan.wu@ieee.org
* Correspondence: job_wangqian@uestc.edu.cn
Abstract:
Cooperative automatic modulation classification (CAMC) using a swarm of sensors is
intriguing nowadays as it would be much more robust than the conventional single-sensing-node
automatic modulation classification (AMC) method. We propose a novel robust CAMC approach
using vectorized soft decision fusion in this work. In each sensing node, the local Hamming distances
between the graph features acquired from the unknown target signal and the training modulation
candidate signals are calculated and transmitted to the fusion center (FC). Then, the global CAMC
decision is made by the indirect vote which is translated from each sensing node’s Hamming-distance
sequence. The simulation results demonstrate that, when the signal-to-noise ratio (SNR) was given by
η ≥
0
dB
, our proposed new CAMC scheme’s correct classification probability
P
cc
could reach up
close to 100%. On the other hand, our proposed new CAMC scheme could significantly outperform
the single-node graph-based AMC technique and the existing decision-level CAMC method in terms
of recognition accuracy, especially in the low-SNR regime.
Keywords:
cooperative automatic modulation classification (CAMC); vectorized decision metrics;
soft-decision-level fusion; graph-based automatic modulation classification; Hamming distance
sequence
1. Introduction
Automatic modulation classification (AMC) mechanisms can enable the frontend
of cognitive ratio technology by blindly identifying the modulation scheme of the trans-
mitted signal. AMC techniques are also very useful in military and civilian applications
such as cognitive radio, adaptive modulation, dynamic spectrum access, surveillance and
electronic warfare [
1
–
6
]. Generally, conventional AMC approaches can be split into two
major categories, (i) the maximum-likelihood-based (ML) approach and (ii) the feature-
recognition-based (FR) approach [
7
]. In practice, the FR methods are more popular than
the ML methods, as the likelihood function of the observed signal data can often be com-
plex and impossible to formulate precisely. On the other hand, the FR approach usually
involves two key steps, namely, feature extraction and modulation classification. Commonly
adopted features include wavelet-related features, cyclic spectrum, high-order statistics,
etc. [
8
–
10
]. Furthermore, the majority of AMC research works in the literature is focused
on the single-sensing-node paradigm, which is quite susceptible to bad channel conditions
and/or high noise levels [11].
In recent years, wireless sensor networks (WSNs) have been emerging as solutions
to many practical applications. Spatially distributed cooperative sensing nodes can infer
more reliable statistical information than any individual sensing node, leading to a much
more robust AMC performance [
12
,
13
]. A cooperative AMC method, though leading to a
Sensors 2022, 22, 1797. https://doi.org/10.3390/s22051797 https://www.mdpi.com/journal/sensors
