Communication signal modulation mode identification method based on graph neural network

Abstract

The invention belongs to the technical field of wireless communication, and discloses a communication signal modulation mode identification method based on a graph neural network. The method includes:transmitting modulation signals of various modulation modes at a transmitting end to obtain a communication signal modulation mode identification data set; dividing the data set according to the number of graph neural network interfaces to obtain a plurality of training sample subsets, inputting the training sample subsets into a feature embedding network one by one, outputting a feature embedding vector of a modulation signal, inputting the feature embedding vector set into the graph neural network, and outputting feature vectors of test samples; and finally, mapping the feature vectors of the test samples into classification results, training the feature embedding network and the graph neural network according to the classification results, and identifying a modulation mode of an unknown modulation signal after the training is completed. According to the method, the problem that an additional data preprocessing means is needed in the prior art is solved, so that the recognition efficiency is improved, and the system complexity is reduced.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to an identification method of a communication signal modulation mode based on a graph neural network. Background technique [0002] At present, the existing technology in the industry is as follows: the communication signal modulation technology can map the transmission information into high-order modulation symbols, improve the transmission rate of the communication system, and realize the spectrum shift of the signal, so that the communication system can adapt to the transmission characteristics of different channels , Improving the reliability of communication is the basic technology of modern communication systems. In non-cooperative communications such as cognitive radio and military electronic countermeasures, our goal is to receive or interfere with the opponent's signal, so we need to use communication signal modulation identification technology t...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that additional data preprocessing is required to extract the high-order cumulants of the signal. There are many types of high-order cumulants, and different types of high-order cumulants are suitable for the identification of modulation methods in different channel environments. Therefore, the choice of high-order statistics type has certain experience and subjectivity.

The shortcomings of this method are: in the case of low signal-to-noise ratio, the noise power of the signal is large, and the original convolutional neural network cannot extract representative underlying features from the noisy signal, and it cannot Make accurate predictions, but the accuracy of modulation identification is low

The disadvantage of this method is that the input data of the convolutional neural network is the constellation diagram corresponding to the complex signal, so the original complex signal needs to be converted into a constellation diagram. This conversion process introduces additional data preprocessing and improves the performance of the algori

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