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Deep learning intelligent modulation identification method based on cyclic spectrum estimation

A technology of deep learning and modulation recognition, applied in character and pattern recognition, computing, computer components, etc., can solve problems such as high complexity and many input parameters, and achieve the effect of improving performance and reducing time complexity

Pending Publication Date: 2019-10-11
SUN YAT SEN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to overcome the technical defects of high complexity and many input parameters in the existing feature parameter extraction and recognition method, the present invention provides a deep learning intelligent modulation recognition method based on cyclic spectrum estimation

Method used

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  • Deep learning intelligent modulation identification method based on cyclic spectrum estimation
  • Deep learning intelligent modulation identification method based on cyclic spectrum estimation
  • Deep learning intelligent modulation identification method based on cyclic spectrum estimation

Examples

Experimental program
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Embodiment 1

[0037] Such as figure 1 As shown, the deep learning intelligent modulation recognition method based on cyclic spectrum estimation includes the following steps:

[0038] S1: Generate a modulated signal according to the carrier frequency and symbol rate;

[0039] S2: Estimate the cyclic spectrum of the modulated signal, and extract the cross-sectional view of the cyclic spectrum function;

[0040] S3: Use the cross-sectional image as a feature to train a deep neural network;

[0041] S4: Use the deep neural network to identify the modulation mode of the unknown signal.

[0042] More specifically, the step S1 is specifically: when the frequency f is used s =16000Hz, carrier frequency f c =5000Hz, symbol rate f d = 1200Hz, generate 2ASK, 2FSK, BPSK, QPSK four modulation signals, and propagate the four modulation signals through the Gaussian channel.

[0043] Wherein, in the step S2, the calculation formula of the cyclic spectrum estimation is specifically:

[0044]

[0045] among them, t Me...

Embodiment 2

[0056] More specifically, on the basis of Embodiment 1, the method of the present invention is further described by taking the identification of four modulation signals of 2ASK, 2FSK, BPSK, and QPSK as an example.

[0057] In the specific implementation process, the frequency f s =16000Hz, carrier frequency f c =5000Hz, symbol rate f d = 1200Hz, generate 2ASK, 2FSK, BPSK, QPSK four modulation signals, and propagate the four modulation signals through the Gaussian channel; calculate the cyclic spectrum of the four signals, and extract the cross section of the cyclic spectrum function And put the corresponding label as the original training data.

[0058] In the specific implementation process, the calculation formula for cyclic spectrum estimation is specifically:

[0059]

[0060] among them, t Means time average statistics; Represents a cross-sectional view of the cyclic spectrum function.

[0061] More specifically, the time smoothing method is used to For estimation, the specif...

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Abstract

The invention provides a deep learning intelligent modulation identification method based on cyclic spectrum estimation. The method comprises the following steps of generating a modulation signal according to a carrier frequency and a code element rate; performing cyclic spectrum estimation on the modulation signal, and extracting a sectional drawing of a cyclic spectrum function; training a deepneural network by taking the sectional view as a feature; and identifying the modulation mode of the unknown signal by using the deep neural network. Cyclic spectrum estimation and the deep neural network are combined, and the performance of the whole signal modulation identification system is improved by utilizing the intelligent processing capability of the neural network and the relatively goodclassification identification capability of the cyclic spectrum. Only the sectional view of the cyclic spectrum function is utilized, the step of extracting cyclic spectrum features is omitted, and the time complexity of the algorithm is reduced.

Description

Technical field [0001] The invention relates to the field of mobile communication, and more specifically, to a deep learning intelligent modulation recognition method based on cyclic spectrum estimation. Background technique [0002] With the development of communication technology, the complexity and unpredictability of the wireless communication environment have greatly increased. Cognitive radio has been widely used because of its cognitive ability, the ability to recognize channel characteristics and dynamically adjust the allocation of system resources. The automatic identification technology of signal modulation is the key technology in the cognitive radio communication system. [0003] The existing communication signal modulation method recognition methods are mainly divided into two categories: the method based on the maximum likelihood theory and the pattern recognition method based on the feature parameter extraction. Because the method based on the maximum likelihood the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/02
CPCG06N3/02G06F18/24
Inventor 张琳刘恒
Owner SUN YAT SEN UNIV
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