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A Modulation Recognition Method Based on Neural Network Ensemble

A technology of modulation recognition and neural network, applied in the field of modulation recognition based on neural network integration, can solve problems such as narrow recognizable range and unsatisfactory recognition performance, and achieve the effect of enhancing recognition performance

Active Publication Date: 2020-12-29
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, most of the pattern recognition methods based on feature extraction are limited by the selected features, and often can only target certain specific modulation methods, and the recognizable range is relatively narrow. The design and selection of features is the biggest obstacle for this type of method.
In addition, most pattern recognition methods based on feature extraction have certain requirements for signal-to-noise ratio, and the recognition performance under low signal-to-noise ratio is not ideal

Method used

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  • A Modulation Recognition Method Based on Neural Network Ensemble
  • A Modulation Recognition Method Based on Neural Network Ensemble
  • A Modulation Recognition Method Based on Neural Network Ensemble

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Embodiment

[0036] The purpose of this embodiment is to identify signals of different modulation modes, and to verify the accuracy of identification. The data in this embodiment comes from the actual satellite communication signal, the signal transmission rate is 2M Baud, including five modulation methods {2PSK, 4PSK, 8PSK, 16QAM, 64QAM}, namely k=5, recorded as i=1,2 ,3,4,5. The receiver is responsible for receiving the signal. The signal-to-noise ratio of the received signal is about 35dB. The received RF signal is converted to a 70M intermediate frequency, and then further down-converted and low-pass filtered to convert it into a baseband signal. 8 symbol symbols are sampled each time to form a 128-dimensional data sample, and the samples are normalized, and 200,000 samples are collected for each signal to form a data set D. For each signal modulation category i∈[k], train a recognizer and output P i (x), then based on P i (x) Build a softmax classifier for recognition. In order to...

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Abstract

The invention belongs to the technical field of communication, and specifically relates to a modulation recognition method based on neural network ensemble. The modulation recognition method based onneural network ensemble utilizes a convolutional neural network to automatically extract the integrated abstract features, avoids the design and selected signal characteristics of a traditional method, and in fact, can obtain different classifiers in the mode of changing a training set so as to adapt to most of modulation modes. In addition, the modulation recognition method based on neural network ensemble uses the ensemble strategy to enhance the recognition performance at a low signal to noise ratio.

Description

technical field [0001] The invention belongs to the technical field of communication, and in particular relates to a modulation recognition method based on neural network integration. Background technique [0002] Since C.S. Weaver and other four scholars published the first article on the automatic modulation recognition of communication signals in the technical report of Stanford University in 1969, the automatic modulation recognition technology of communication signals has been a research hotspot in the field of communication. Reconnaissance and countermeasures, spectrum monitoring and management and other fields have a wide range of applications, and are of great significance to communication intelligence. Existing modulation recognition techniques can be divided into two categories: maximum likelihood methods based on hypothesis testing and pattern recognition methods based on feature extraction. [0003] The maximum likelihood method based on hypothesis testing is a ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L27/00
CPCH04L27/0008H04L27/0012
Inventor 王卫东马俊虎廖红舒甘露
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA