Citation: Huang, H.; Wang, J.; Li, J.
FFSCN: Frame Fusion Spectrum
Center Net for Carrier Signal
Detection. Electronics 2022, 11, 3349.
https://doi.org/10.3390/
electronics11203349
Academic Editors: Phivos Mylonas,
Katia Lida Kermanidis and
Manolis Maragoudakis
Received: 23 September 2022
Accepted: 14 October 2022
Published: 17 October 2022
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Article
FFSCN: Frame Fusion Spectrum Center Net for Carrier
Signal Detection
Hao Huang , Jiao Wang and Jianqing Li *
School of Electronic Science and Engineering, University of Electronic Science and Technology of China,
Chengdu 611731, China
* Correspondence: lijq@uestc.edu.cn
Abstract:
Carrier signal detection is a complicated and essential task in many domains because it
demands a quick response to the existence of several carriers in the wideband, while also precisely
predicting each carrier signal’s frequency centers and bandwidths, including single-carrier and
multi-carrier modulation signals. Multi-carrier modulation signals, such as FSK and OFDM, could
be incorrectly recognized as several single-carrier signals by using the spectrum center net (SCN) or
FCN-based method. This paper designed a deep convolutional neural network (CNN) framework for
multi-carrier signal detection by fusing the features of multiple consecutive frames of the broadband
power spectra and estimating the information of each single-carrier or multi-carrier modulation
signal in the broadband, called frame fusion spectrum center net (FFSCN), including FFSCN-R,
FFSCN-MN, and FFSCN-FMN. FFSCN includes three base parts, the deep CNN-based backbone,
the feature pyramid network (FPN) neck, and the regression network (RegNet) head. FFSCN-R
and FFSCN-MN fusing the FPN out features, which use the Residual and MobileNetV3 backbone,
respectively, and FFSCN-MN cost less inference time. To further reduce the complexity of FFSCN-MN,
the designed FFSCN-FMN modifies the MobileNet blocks and fuses the features at each block of
the backbone. The multiple consecutive frames of broadband power spectra not only preserve the
high-resolution ratio of the broadband frequency, but also add the features of the signal changes in the
time dimension. Extensive experimental results demonstrate that the proposed FFSCN can effectively
detect multi-carrier and single-carrier modulation signals in the broadband power spectrum and
outperform SCN in accuracy and efficiency.
Keywords: carrier signal detection; frame fusion; deep learning; convolutional neural network
1. Introduction
Carrier signal detection in the wideband is usually the first and most vital step of blind
communication signal processing. For further study, each sub-carrier signal demodulation,
channel decoding, and other subsequent analyses, accurate carrier signal detection in the
wideband is a prerequisite.
Similar to the primary signal detection in cognitive radio (CR) [
1
], carrier signal
detection often requires the timely and precise detection of all sub-carrier signals in a non-
cooperative communication environment in a wideband signal, which can be formulated
as follows [2]:
Y(n) =
W(n), H
0
M
∑
i=1
S
i
(n) + W(n), H
1
(1)
where
Y(n)
denotes the received non-cooperative wideband signal,
S
i
(n)
is the
i
th
sub-
carrier signal,
M
denotes the numbers of all sub-carrier signals in the received wideband
signal,
W(n)
denotes the received noise, which can be modeled as the zero-mean additive
white Gaussian noise (AWGN), and
H
0
and
H
1
denote the hypothesis of the absence and
the presence, respectively, of the sub-carrier signal in the received wideband signal.
Electronics 2022, 11, 3349. https://doi.org/10.3390/electronics11203349 https://www.mdpi.com/journal/electronics
