Modulated signal time-frequency diagram classification system based on generative adversarial network and operation method thereof

Abstract

The invention relates to a modulated signal time-frequency diagram classification system based on a generative adversarial network and an operation method of the modulated signal time-frequency diagram classification system. The modulated signal time-frequency diagram classification system comprises an IQ signal time-frequency diagram conversion module, a generator module, a discriminator module and an auxiliary classifier module, wherein the IQ signal time-frequency diagram conversion module converts an original signal into a signal short-time Fourier time-frequency diagram form; the generator module maps an original noise vector and category information input by means of the original noise vector into a corresponding short-time Fourier time-frequency graph; the discriminator module is used for receiving real picture data and picture data generated by a discriminator, and outputting confidence probabilities which are different in input and correspond to each other and are discriminated to be true; and the auxiliary classifier module receives a high-dimensional feature map extracted by a convolution layer and outputs corresponding category information. The operation method is usedfor solving the problem that a deep neural network model for signal classification is trained but the number of data set samples is insufficient.

Description

technical field [0001] The invention relates to a modulation signal time-frequency graph classification system based on a generative confrontation network and an operating method thereof, belonging to the technical field of big data and artificial intelligence. Background technique [0002] In recent years, with the rapid improvement of the performance of hardware-level computing units, deep learning has also demonstrated incredible capabilities in various fields, especially in the fields of image and natural language processing. With the proposal of various new network structures, more and more scholars try to apply the neural network model to the field of signal analysis to classify different types of modulation signals. [0003] As we all know, the training of neural networks requires a large number of data set samples as support, that is, a large number of signal samples are used to train and verify the model. However, in many scenarios, signal acquisition requires a lo...

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Problems solved by technology

[0005] In view of the current ubiquitous problem of insufficient training signal classification neural network data sets and the inability to dig deep into signal features, the present invention provides a modulation signal time-frequency diagram classification system and its operating method based on a generative adversarial network, so as to achieve training with A certain robust neural network classification model provides researchers engaged in related research with a convenient and quick way to mine deep signal features and data set expansion methods

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