Modulation recognizing method and system based on order statistics and machine learning

A technology of sequential statistics and modulation identification, applied in modulation carrier system, modulation type identification, transmission system, etc., can solve the problems of poor classification performance and low computational complexity, and achieve the effect of good computational complexity

Active Publication Date: 2016-06-08
TSINGHUA UNIV +1
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  • Application Information

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Problems solved by technology

[0007] The technical problem to be solved by the present invention is: the existing modu...

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  • Modulation recognizing method and system based on order statistics and machine learning
  • Modulation recognizing method and system based on order statistics and machine learning
  • Modulation recognizing method and system based on order statistics and machine learning

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Embodiment Construction

[0043] Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0044] Order statistics are the values ​​of real-valued variables in ascending order, which can retain all information except the order of the original data, and can be used as a feature to classify modulation types.

[0045] figure 1 A schematic flowchart of a modulation recognition method based on order statistics and machine learning according to an embodiment of the present invention is shown. Such as figure 1 As shown, the method includes:

[0046] S11: Obtain the amplitude and phase of the signal to be identified, respectively sort the amplitude and phase of each signal to be identified, and obtain the order statistics of the amplitude and the order statistics of the phase;

[0047] S12: Using a machine learning model to classify the modulation type of the signal to be identified according to the order statistics of the amplitude and the order sta...

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Abstract

The invention relates to a modulation recognizing method and system based on order statistics and machine learning. The method includes the steps of obtaining the amplitude and phase of a to-be-recognized signal, obtaining the order statistics of the amplitude and the order statistics of the phase, classifying modulation types of the to-be-recognized signal through a robot learning model according to the order statistics of the amplitude and the order statistics of the phase to distinguish a QAM signal and different PSK signals, obtaining the real part and the visual part of the to-be-recognized QAM signal, obtaining the order statistics of the real part of the to-be-recognized QAM signal and the order statistics of the visual part of the to-be-recognized QAM signal, and classifying the modulation types of the to-be-recognized QAM signal through the robot learning model according to the order statistics of the real part of the to-be-recognized QAM signal and the order statistics of the visual part of the to-be-recognized QAM signal to distinguish different QAM signals. Good signal classifying performance is kept when the computation complexity is low.

Description

technical field [0001] The present invention relates to the technical field of wireless communication, in particular to a modulation recognition method and system based on order statistics and machine learning. Background technique [0002] Wireless communication is an important way of information transmission in our lives. From the early radio and TV broadcast signals to the current Wi-Fi and 4G communication technology used by mobile phones, wireless communication technology is increasingly affecting our life. The modulation and demodulation of communication signals is an important field in wireless communication. [0003] Since low-frequency wireless signals are not suitable for transmission and reception, we need to load the transmitted signal on a high-frequency carrier in a certain way to make it suitable for air propagation. This process is signal modulation. Due to different communication purposes, communication environments and other conditions of wireless communi...

Claims

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Application Information

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IPC IPC(8): H04L27/00
CPCH04L27/0012
Inventor 高飞飞韩露冰李赞
Owner TSINGHUA UNIV
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