Modulation signal identification method and system based on deep learning
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A modulated signal and deep learning technology, which is applied in the field of communication technology applications, can solve the problems of reducing the recognition accuracy of modulated signals, large noise influence, and large computational complexity.
Pending Publication Date: 2022-02-08
SUN YAT SEN UNIV
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Literature [2] B. Ramkumar, "Automatic modulation classification for cognitive radios using cyclic feature detection," in IEEE Circuits and Systems Magazine, vol.9, no.2, pp.27-45, Second Quarter 2009, doi:10.1109/MCAS. 2008.931739. Using cyclic spectrum feature analysis and decision tree-based classifier to realize the modulation type identification of the signal, but this method needs to manually extract several features of the cyclic spectrum, and the computational complexity is relatively large
In order to overcome th
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Embodiment 1
[0078] Such as figure 1 As shown, a modulation signal recognition method based on deep learning includes the following steps:
[0079] S1: Generate different types of modulation signals containing noise;
[0080] S2: performing Wiener filtering on the modulated signal containing noise, and performing noise reduction processing;
[0081] S3: Estimating the cyclic spectrum of the modulated signal after noise reduction, and extracting a two-dimensional cross-sectional view of the cyclic spectrum;
[0082] S4: Construct a deep neural network, input the two-dimensional cross-sectional graph of the cyclic spectrum into the deep neural network as an input feature, and train the deep neural network;
[0083] S5: Use the trained deep neural network to identify the modulation mode of the unknown signal.
[0084] In the specific implementation process, this scheme first performs noise reduction processing on the modulated signal through Wiener filtering, which can effectively reduce t...
Embodiment 2
[0116] More specifically, on the basis of Embodiment 1, this solution also provides a modulated signal identification system based on deep learning, which is used to implement a modulated signal identification method based on deep learning, specifically including a noise-containing modulated signal generation module, Wiener filtering module, cyclic spectrum estimation module, neural network building module, neural network training module, identification module; where:
[0117] The noise-containing modulation signal generation module is used to generate different types of noise-containing modulation signals;
[0118] The Wiener filtering module is used to perform Wiener filtering on the noise-containing modulation signal to perform noise reduction processing;
[0119] The cyclic spectrum estimation module is used to perform cyclic spectrum estimation on the modulated signal after noise reduction, and extract a two-dimensional cross-sectional view of the cyclic spectrum;
[012...
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Abstract
The invention provides a modulation signal identification method based on deep learning. The method comprises the following steps: generating different types of modulation signals containing noise; carrying out wiener filtering noise reduction on the noise-containing modulation signal; performing cyclic spectrum estimation on the modulated signal after noise reduction, and extracting a cyclic spectrum two-dimensional sectional view; constructing a deep neural network, inputting the cyclic spectrum two-dimensional sectional view as an input feature into the deep neural network, and training the deep neural network; and identifying a modulation mode of an unknown signal by using the trained deep neural network. The invention further provides a modulation signal recognition system based on deep learning, noise reduction processing is performed on the modulation signal through Wiener filtering, and the influence of noise on recognition precision can be effectively reduced; meanwhile, the cyclic spectrum two-dimensional sectional view is used as an input feature, on one hand, the cyclic spectrum two-dimensional sectional view is not sensitive to noise, so that the influence of the noise on an identification result can be effectively reduced, and on the other hand, the complexity of an algorithm can be greatly reduced, and the identification efficiency can be improved.
Description
technical field [0001] The present invention relates to the technical field of communication technology application, in particular to a modulation signal recognition method and system based on deep learning. Background technique [0002] The purpose of modulation recognition technologies is to identify modulation schemes from received or intercepted signals in a short period of time in civilian or military applications, thus, they can make radio transceivers aware of application scenarios such as surveillance and electronic warfare. Modulation recognition algorithms can be divided into two main types, one is based on maximum likelihood and the other is based on feature extraction. Compared with feature extraction methods, maximum likelihood methods can achieve better recognition accuracy, but require prior knowledge of channel state information (CSI) or statistical properties of received signals. In contrast, feature extraction systems do not require priority information, s...
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