An automatic modulation recognition method, device, electronic equipment and storage medium
A modulation identification and automatic technology, applied in the field of signal analysis, can solve problems such as inability to work, inability to directly apply modulation signals, and inconspicuous visual features of modulation signals, etc., to achieve the effect of reducing difficulty
Active Publication Date: 2021-11-02
BEIJING UNIV OF POSTS & TELECOMM
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Problems solved by technology
[0002] In the prior art, deep learning-based algorithms need to be trained with massive training sets to obtain superior classification performance. However, in non-cooperative communication methods, there are fewer training samples, and even some types of modulation signals are not Samples exist, yet state-of-the-art deep learning algorithms do not work under zero-sample conditions for automatic modulation recognition tasks
[0003] However, zero-shot learning tries to improve the influence of zero samples on deep learning models by connecting various image categories in semantic descriptions. However, images can be described semantically through visual attributes, and the visual features of the modulation signal are not obvious, so visual attributes cannot be used. For semantic description, it cannot be directly applied to the modulated signal
Therefore, the prior art has the problem of being unable to perform automatic modulation recognition under zero-sample conditions
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[0050] In order to make the purpose, technical solutions and advantages of the present disclosure clearer, the present disclosure will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.
[0051]It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present application shall have common meanings understood by those skilled in the art to which the present disclosure belongs. "First", "second" and similar words used in one or more embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Comprising" or "comprising" and similar words mean that the elements or items appearing before the word include the elements or items listed after the word and their equivalents, without excluding other elements or items. Words such as "connected" or "connected"...
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One or more embodiments in the present application provide an automatic modulation recognition method, device, electronic equipment, and storage medium, including: obtaining the signal sample to be tested, real sample category data, and semantic vector; obtaining a generative confrontation network model; The sample category semantic vector input generates an adversarial network model to generate zero-sample category data; obtains the classifier model; inputs the signal sample to be tested into the classifier model to obtain the recognition result. In this application, the adversarial network model is generated by using the semantic vector with sample categories to generate zero-sample category data through the adversarial network model to solve the problem that some modulation signals have no samples. While obtaining a classifier model with superior classification performance, Generate zero-sample category data by generating an adversarial network model to reduce the difficulty of collecting training data.
Description
technical field [0001] One or more embodiments of the present application relate to the technical field of signal analysis, and in particular, to an automatic modulation recognition method, device, electronic equipment, and storage medium. Background technique [0002] In the prior art, deep learning-based algorithms need to be trained with massive training sets to obtain superior classification performance. However, in non-cooperative communication methods, there are fewer training samples, and even some types of modulation signals are not Samples exist, yet state-of-the-art deep learning algorithms do not work under zero-sample conditions for automatic modulation recognition tasks. [0003] However, zero-shot learning tries to improve the influence of zero samples on deep learning models by connecting various image categories in semantic descriptions. However, images can be described semantically through visual attributes, and the visual features of the modulation signal a...
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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F2218/08G06F2218/12G06F18/2414
Inventor 景晓军周全张芳沛崔原豪张荣辉李海涵朱家穆俊生
Owner BEIJING UNIV OF POSTS & TELECOMM