Signal modulation recognition algorithm based on data enhancement and convolutional neural network

A convolutional neural network and signal modulation technology, applied in the field of communication, which can solve problems such as a large amount of training data

Inactive Publication Date: 2021-10-22
CHONGQING UNIV OF POSTS & TELECOMM
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AI Technical Summary

Problems solved by technology

[0005] Although deep learning based methods can greatly improve the performance of modulation classifiers, it requires a large amount of training data

Method used

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  • Signal modulation recognition algorithm based on data enhancement and convolutional neural network
  • Signal modulation recognition algorithm based on data enhancement and convolutional neural network
  • Signal modulation recognition algorithm based on data enhancement and convolutional neural network

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Embodiment Construction

[0048] The present invention first processes the time-domain signal in the public data set RML2016.10a into a constellation diagram representation, and then divides the constellation diagram into a training set and a test set in a ratio of 8:2, and then utilizes four data enhancement methods (rotation, random erasing, etc.) Divide, flip, CutMix) to expand the training set, in which the rotation is expanded to 4 times the original training set, the flip is expanded to 3 times the original data set, and the probability of random erasing is 0.5. Finally, based on the GoogleNet network, the enhanced The training set is sent to the network for training, and the network parameters are continuously back-propagated through the loss function and the accuracy rate to optimize the network parameters, and finally the trained network is obtained, and the network is tested based on the test set to obtain the accuracy rate of the test set. The concrete implementation process of the present in...

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Abstract

In order to improve the accuracy of modulation recognition, the invention provides a signal modulation recognition algorithm based on data enhancement and a convolutional neural network. The method comprises the following steps: firstly, processing a time domain signal in a data set RML2016.10a into a constellation diagram, and dividing pictures into a training set and a test set according to a proportion of 8: 2; secondly, processing the training sets by using four data enhancement methods of rotation, random erasing, overturning and CutMix to obtain four enhanced training sets; respectively inputting the four types of enhanced training sets into a GoogleNet network for training, and continuously optimizing network parameters according to back propagation to obtain a trained network; and finally, sending the test set into the trained GoogleNet network for testing, drawing a curve of which the correct rate changes along with the signal-to-noise ratio, and comparing the improvement effects of different data enhancement methods on the correct recognition rate to obtain an optimal data enhancement method. Compared with other modulation recognition algorithms without data enhancement, the method has the advantages that the risk of model overfitting is reduced, and the generalization ability of the model is improved.

Description

technical field [0001] The convolutional neural network related algorithm involved in the present invention belongs to the field of computer vision and artificial intelligence, the data enhancement belongs to the field of image processing, and the signal modulation recognition belongs to the field of communication technology. Background technique [0002] Automatic Modulation Classification (AMC) refers to the process of knowing the modulation set of the signal, using information technology and computer technology to analyze the modulated target signal to determine the signal modulation type, which is used in both military and civilian fields. Has quite a wide range of applications. In the military field, automatic modulation classification can be applied to applications such as communication countermeasures and electronic reconnaissance. During communication countermeasures, it is necessary to detect, interfere and demodulate enemy signals to steal important enemy intellige...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F2218/08G06F18/214
Inventor 张承畅徐余余洒
Owner CHONGQING UNIV OF POSTS & TELECOMM
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