[1]GAO Hongyuan,WANG Shihao,CHENG Jianhua,et al.Modulation signal recognition based on evolutionary long short-term memory neural network under impulse noise[J].CAAI Transactions on Intelligent Systems,2023,18(4):676-687.[doi:10.11992/tis.202205002]
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Modulation signal recognition based on evolutionary long short-term memory neural network under impulse noise

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 4 Page number: 676-687 Column: 学术论文—机器学习 Public date: 2023-07-15
Title:
Modulation signal recognition based on evolutionary long short-term memory neural network under impulse noise
Author(s):
GAO Hongyuan1;  WANG Shihao1;  CHENG Jianhua2;  GUO Ruichen1;  ZHANG Zhiwei1
1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;
2. College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
modulating signal recognition; impulsive noise; convolution neural network (CNN); quantum sailfish optimization algorithm (QSFA); long short-term memory (LSTM) neural network; stable distribution; hyper parameters; short time Fourier transform
CLC:
TP183; TN911.7
DOI:
10.11992/tis.202205002
Abstract:
In order to solve the problems of weak resistance against impulsve noise and difficulty in determining hyperparameters of the modulation signal recognition method with long short-term memory (LSTM) neural network under impulse noise, this paper presents a modulation recognition method based on evolutionary LSTM neural network. The convolution neural network (CNN) denoising model based on short-time Fourier transform is used to denoise the data set. Then, combined with the quantum computation mechanism and sailfish optimizer (SFO), the quantum sailfish algorithm (QSFA) is designed to evolve LSTM neural network to obtain the optimal hyper-parameters. An evolutionary LSTM neural network is used as a classifier for automatic modulating signal recognition. Simulation results show that the recognition accuracy is greatly improved by using the designed CNN denoising and evolutionary LSTM neural network model. Moreover, the evolutionary LSTM neural network model based on quantum sailfish algorithm reduces the probability that traditional LSTM neural network is easy to fall into local minimum or over fitting. When the mixed signal-to-noise ratio (MSNR) is 0 dB, the average recognition accuracy of the proposed method for 11 modulated signals is more than 90%.
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