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A spatiotemporal multi-channel deep learning system for automatic modulation recognition

A modulation recognition and deep learning technology, applied in the field of automatic modulation recognition, can solve the problems of low recognition performance, achieve high scalability, improve recognition accuracy, and improve recognition performance

Active Publication Date: 2021-01-26
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] In view of the above-mentioned deficiencies in the prior art, a spatio-temporal multi-channel deep learning system for automatic modulation recognition provided by the present invention solves the problem of low recognition performance of other deep learning systems

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  • A spatiotemporal multi-channel deep learning system for automatic modulation recognition
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  • A spatiotemporal multi-channel deep learning system for automatic modulation recognition

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Embodiment Construction

[0023] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0024] The signal model of the present invention is a single-input single-output communication system, which can be expressed as:

[0025] r(t)=s(t)*h(t)+n(t)

[0026] Among them, r(t) is the modulated signal received by the receiver, s(t) is the modulated signal transmitted by the transmitter, h(t) is the channel impulse response, n(t) is the additive white Gaussian noise, r(t ) is sampled n times by an A / D converter at a...

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Abstract

The invention discloses a spatio-temporal multi-channel deep learning system for automatic modulation recognition, which comprises a sequentially connected multi-channel input and spatial feature mapping module, a temporal feature extraction module and a fully connected network classifier module. The system further learns the spatial and temporal characteristics of the modulated signal through the cascaded structure of CNN and LSTM, so that the system can better and more comprehensively extract the spatial and temporal features of the modulated signal, and deepen the mapping relationship between the hidden layer and the output. The invention improves the recognition accuracy of the automatic modulation recognition based on the deep learning method, and significantly improves the recognition performance of high-order modulation types. The system is also highly scalable and can input more different types of data with modulation information by adding input layers.

Description

technical field [0001] The invention relates to the technical field of automatic modulation recognition, in particular to a space-time multi-channel deep learning system for automatic modulation recognition. Background technique [0002] The purpose of the automatic modulation recognition (Automatic Modulation Recognition, AMR) technology is to automatically identify the modulation type of the noisy modulation signal. As a key step between signal detection and demodulation, it is the prerequisite guarantee for information extraction. At present, AMR has been widely used in various fields, such as cognitive radio, spectrum management, electronic monitoring and so on. [0003] Generally, there are two traditional methods for automatic modulation recognition: one is based on decision theory, and the other is based on feature learning. Decision theory-based methods rely heavily on prior knowledge and parameter estimates. Feature-based methods usually consist of a feature extr...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F2218/04G06F2218/12G06F2218/08
Inventor 骆春波徐加朗罗杨孙文健刘子健吴佳刘翔许燕濮希同韦仕才张赟疆
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA