Citation: Huang, H.; Wang, P.; Wang,
J.; Li, J. Deep Learning-Based
End-to-End Carrier Signal Detection
in Broadband Power Spectrum.
Electronics 2022, 11, 1896. https://
doi.org/10.3390/electronics11121896
Academic Editors: Phivos Mylonas,
Katia Lida Kermanidis and
Manolis Maragoudakis
Received: 24 May 2022
Accepted: 14 June 2022
Published: 16 June 2022
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Article
Deep Learning-Based End-to-End Carrier Signal Detection in
Broadband Power Spectrum
Hao Huang
1
, Peng Wang
2
, Jiao Wang
1
and Jianqing Li
1,
*
1
The School of Electronic Science and Engineering, University of Electronic Science and Technology of China,
Chengdu 611054, China; huanghao@std.uestc.edu.cn (H.H.); 201811022520@std.uestc.edu.cn (J.W.)
2
Guangzhou Haige Communications Group Incorporated Company, Guangzhou 510663, China;
peng.wang@haige.com
* Correspondence: lijq@uestc.edu.cn
Abstract:
This paper presents an end-to-end deep convolutional neural network (CNN) model for
carrier signal detection in the broadband power spectrum, so-called spectrum center net (SCN).
By regarding the broadband power spectrum sequence as a one-dimensional (1D) image and each
subcarrier on the broadband as the target object, we can transform the carrier signal detection problem
into a semantic segmentation problem on a 1D image. Here, the core task of the carrier signal detection
problem turns into the frequency center (FC) and bandwidth (BW) regression. We design the SCN
to classify the broadband power spectrum as inputs and extract the features of different length
scales by the ResNet backbone. Then, the feature pyramid network (FPN) neck fuses the features
and outputs the fusion features. Next, the RegNet head regresses the power spectrum distribution
(PSD) prediction for FC and the corresponding BW prediction. Finally, we can achieve the subcarrier
targets by applying non-maximum suppressions (NMS). Moreover, we train the SCN on a simulation
dataset and validate it on a real satellite broadband power spectrum set. As an improvement of
the fully convolutional network-based (FCN-based) method, the proposed method directly outputs
the detection results without post-processing. Extensive experimental results demonstrate that the
proposed method can effectively detect the subcarrier signal in the broadband power spectrum
as well as achieve higher and more robust performance than the deep FCN- and threshold-based
methods.
Keywords:
carrier signal detection; broadband power spectrum; deep learning; convolutional net-
works; regression
1. Introduction
With the rapid development of wireless mobile communication, satellite communi-
cation, and other communication technologies, the electromagnetic spectrum space has
become very complex and crowded. The safe use and effective control of the electromag-
netic spectrum have turned into the critical task of radio monitoring, particularly in the
non-cooperative electromagnetic spectrum monitoring field. Carrier signal detection is
the first and most crucial step of non-cooperative signal processing. Through accurately
detecting only the signal in the spectrum, we can further perform modulation recognition,
channel coding identification, source coding identification, specific emitter identification,
and other information analysis processes.
Few algorithms [
1
–
6
] are available for carrier signal detection, and these algorithms are
mainly based on threshold values and human intervention, although some improvements
have been noted using the double-thresholds method [
7
,
8
]. Kim et al. [
9
] proposed the use
of a slope tracing-based algorithm to separate the interval of signal elements based on signal
properties, such as amplitude, slope, deflection width or distance between neighboring
deflections. For the practical application of these methods, many restrictions exist due to
Electronics 2022, 11, 1896. https://doi.org/10.3390/electronics11121896 https://www.mdpi.com/journal/electronics
