2021
DOI: 10.1109/access.2021.3079131
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A New Modulation Recognition Method Based on Flying Fish Swarm Algorithm

Abstract: 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 … Show more

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Cited by 4 publications
(3 citation statements)
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“…Among these, σ(σ >0) is known as the scaling factor, which substitutes (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11) into the unified expression of the Cohen class time-frequency distribution. If the continuous signal is…”
Section: Cwd Time-frequency Analysis Image Generationmentioning
confidence: 99%
See 1 more Smart Citation
“…Among these, σ(σ >0) is known as the scaling factor, which substitutes (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11) into the unified expression of the Cohen class time-frequency distribution. If the continuous signal is…”
Section: Cwd Time-frequency Analysis Image Generationmentioning
confidence: 99%
“…The accuracy of this method is 90% at −5 dB. The method in [ 7 ] integrates multidimensional information of CWD, STFT, and cyclic spectrum, adopts three kinds of neural networks and flying fish swarm intelligence algorithms to identify the modulation types of signals, and the recognition rate reaches 84.7% at −4 dB. The authors of [ 8 ] studied the autocorrelation function of the signal and used CNN-DNN to identify the two-dimensional cross-section features, with a high recognition accuracy at −5 dB.…”
Section: Introductionmentioning
confidence: 99%
“…Most scholars use the time-frequency domain analysis method to extract fault signal features. Time-frequency domain analysis methods mainly make use of the joint distribution of time domain information and frequency domain information to conduct correlation analysis of signals, among which wavelet transform [ 2 ], singular value decomposition [ 3 ], short-time Fourier transform [ 4 ], wavelet packet transform [ 5 ], ensemble empirical mode decomposition [ 6 ], and other time-frequency analysis methods are widely used in the field of fault detection. However, although the abovementioned methods claim certain achievements, there are still some common limitations: the first is the feature extraction conducted mainly through technical personnel artificial extraction, which relies on the expert’s experience and lack of generalization, especially when the power equipment is more complex or in operation mode, etc., making the traditional feature extraction method difficult to effectively extract the fault characteristic information; Secondly, most of the current feature extraction methods are equipped with shallow classification models [ 7 , 8 , 9 ], and the simple architecture of these models limits the nonlinear processing of fault feature information.…”
Section: Introductionmentioning
confidence: 99%