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Digital modulation signal identification method based on deep cooperative training

A digitally modulated signal and collaborative training technology, applied in the field of deep learning, can solve problems such as high cost, difficulty in obtaining labeled samples, and insufficient use of unlabeled samples, so as to achieve the effect of improving generalization ability and strong robustness

Pending Publication Date: 2021-11-26
UNIV OF ELECTRONIC SCI & TECH OF CHINA +1
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Problems solved by technology

In practice, only a large number of unlabeled samples can be obtained, and it is very difficult and expensive to obtain labeled samples
General feature-based modulation recognition methods do not take full advantage of the large number of unlabeled samples

Method used

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  • Digital modulation signal identification method based on deep cooperative training
  • Digital modulation signal identification method based on deep cooperative training
  • Digital modulation signal identification method based on deep cooperative training

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Embodiment Construction

[0028] In the content of the invention, the technical solution of the present invention has been described in detail. On the basis of making full use of a small amount of labeled data information, the method of the present invention extracts and utilizes a large amount of unlabeled data information, so that the feature extractor can use a small amount of labeled data. Excellent performance can also be obtained in the sample case, which improves the practicability of the digital signal modulation recognition method.

[0029] In the digital signal modulation recognition method based on deep collaborative training of the present invention, the data set is expanded and the network model is generalized through signal preprocessing first, and the generation of confrontational data is realized through a generative confrontation algorithm. Adversarial data can be thought of as a view of signal samples different from the original samples, and the assumption of co-training can be satisfi...

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Abstract

The invention belongs to the technical field of deep learning, and particularly relates to a digital modulation signal identification method based on deep cooperative training. The traditional digital modulation signal identification method based on deep learning needs a large number of marked data samples for training. In actual communication activities, only a large number of unmarked signal samples are easy to obtain, and manual marking is high in cost and low in efficiency. Two CLDNN networks are established for cooperative training, the differentiation of views is realized by utilizing generative adversarial, a large number of unmarked samples are fully utilized to assist learning under a small number of marked samples, the recognition accuracy is effectively improved, and the feasibility and practicability of deep learning in a digital modulation signal recognition task are enhanced.

Description

technical field [0001] The invention belongs to the technical field of deep learning, and in particular relates to a digital modulation signal recognition method based on deep collaborative training. Background technique [0002] Digital signal modulation recognition is an important technology in the field of wireless communication, and it is widely used in civil and military fields. The task of digital signal modulation recognition is mainly to confirm the modulation type of the signal with only a small amount of prior information or without prior information, which is an important step between signal detection and demodulation. With the rapid development of wireless communication, signal modulation methods have become diverse, and the cost of modulation identification has also increased. [0003] Modulation recognition methods can generally be classified into two categories: likelihood ratio-based modulation recognition methods and feature-based modulation recognition met...

Claims

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Application Information

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IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/084G06N3/045Y02D30/70
Inventor 罗程王卫东甘露廖红舒
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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