A New Modulation Recognition Method Based on Flying Fish Swarm Algorithm

The modulation recognition method based on deep learning plays a significant role in the intelligent communication system. To further improve the recognition rate, especially in the case of small samples with a low signal-to-noise ratio, this paper proposes a new modulation recognition method based...

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Autores principales:	Jingpeng Gao, Xu Wang, Ruowu Wu, Xiong Xu
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Modulation recognition deep learning flying fish swarm algorithm multi-channel signal processing feature extraction Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/f89976023c504af582b48899eb16f1cb
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id	oai:doaj.org-article:f89976023c504af582b48899eb16f1cb
record_format	dspace
spelling	oai:doaj.org-article:f89976023c504af582b48899eb16f1cb2021-11-19T00:06:56ZA New Modulation Recognition Method Based on Flying Fish Swarm Algorithm2169-353610.1109/ACCESS.2021.3079131https://doaj.org/article/f89976023c504af582b48899eb16f1cb2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9427550/https://doaj.org/toc/2169-3536The modulation recognition method based on deep learning plays a significant role in the intelligent communication system. To further improve the recognition rate, especially in the case of small samples with a low signal-to-noise ratio, this paper proposes a new modulation recognition method based on flying fish swarm algorithm. First, Short-Time Fourier Transform, Choi-Williams Distribution, and Cyclic Spectrum are combined to complete multi-channel signal processing. Second, AlexNet, VGGNet, GoogLeNet, and ResNet are transferred to realize feature extraction. Third, the support vector machine classifies the modulations after dimension reduction and feature fusion. Finally, the flying fish swarm is proposed to optimize the signal processing methods, the types of networks, the layers of networks, the dimensions of features, and the parameters of the support vector machine. The method can accurately recognize BPSK, QPSK, OQPSK, 8PSK, 4ASK, QAM16, QAM32, and QAM64. The simulation results show that the average recognition rate of modulation is 94.5% at SNR of 0 dB and 84.7% at SNR of −4 dB. Besides, the proposed modulation recognition method possesses good robustness under low SNR conditions.Jingpeng GaoXu WangRuowu WuXiong XuIEEEarticleModulation recognitiondeep learningflying fish swarm algorithmmulti-channel signal processingfeature extractionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 76689-76706 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Modulation recognition deep learning flying fish swarm algorithm multi-channel signal processing feature extraction Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Modulation recognition deep learning flying fish swarm algorithm multi-channel signal processing feature extraction Electrical engineering. Electronics. Nuclear engineering TK1-9971 Jingpeng Gao Xu Wang Ruowu Wu Xiong Xu A New Modulation Recognition Method Based on Flying Fish Swarm Algorithm
description	The modulation recognition method based on deep learning plays a significant role in the intelligent communication system. To further improve the recognition rate, especially in the case of small samples with a low signal-to-noise ratio, this paper proposes a new modulation recognition method based on flying fish swarm algorithm. First, Short-Time Fourier Transform, Choi-Williams Distribution, and Cyclic Spectrum are combined to complete multi-channel signal processing. Second, AlexNet, VGGNet, GoogLeNet, and ResNet are transferred to realize feature extraction. Third, the support vector machine classifies the modulations after dimension reduction and feature fusion. Finally, the flying fish swarm is proposed to optimize the signal processing methods, the types of networks, the layers of networks, the dimensions of features, and the parameters of the support vector machine. The method can accurately recognize BPSK, QPSK, OQPSK, 8PSK, 4ASK, QAM16, QAM32, and QAM64. The simulation results show that the average recognition rate of modulation is 94.5% at SNR of 0 dB and 84.7% at SNR of −4 dB. Besides, the proposed modulation recognition method possesses good robustness under low SNR conditions.
format	article
author	Jingpeng Gao Xu Wang Ruowu Wu Xiong Xu
author_facet	Jingpeng Gao Xu Wang Ruowu Wu Xiong Xu
author_sort	Jingpeng Gao
title	A New Modulation Recognition Method Based on Flying Fish Swarm Algorithm
title_short	A New Modulation Recognition Method Based on Flying Fish Swarm Algorithm
title_full	A New Modulation Recognition Method Based on Flying Fish Swarm Algorithm
title_fullStr	A New Modulation Recognition Method Based on Flying Fish Swarm Algorithm
title_full_unstemmed	A New Modulation Recognition Method Based on Flying Fish Swarm Algorithm
title_sort	new modulation recognition method based on flying fish swarm algorithm
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/f89976023c504af582b48899eb16f1cb
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_version_	1718420645509857280

A New Modulation Recognition Method Based on Flying Fish Swarm Algorithm

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