Method and system for identifying signal modulation mode based on convolutional neural network

Abstract

The invention discloses a method and a system for identifying a signal modulation mode based on a convolutional neural network, and belongs to the technical field of signal detection and identification. The method comprises the following steps: adding noise to one of two paths of noiseless signals sent by a signal source; generating a high-order cumulant and a two-dimensional matrix as training labels, and generating a high-order cumulant and a two-dimensional matrix as data input quantities; obtaining a plurality of de-noising feature models, and generating an identification model; and obtaining a signal sent by the signal source, extracting I / Q information, truncating the high-order cumulant of the I / Q information, generating a two-dimensional matrix, sending the two-dimensional matrix into the identification model to carry out modulation identification on the signal, and outputting a signal modulation mode. According to the method, the generalization capability and the identification accuracy of the classifier are improved. The number of actually received signal samples is reduced, the influence of noise is effectively inhibited by using unsupervised de-noising self-coding, andthe accuracy of a final identification model is improved.

Description

technical field [0001] The present invention relates to the technical field of signal detection and identification, and more specifically, to a method and system for identifying signal modulation modes based on a convolutional neural network. Background technique [0002] With the rapid development of wireless communication and its wide application, spectrum resources are increasingly scarce. There are many ways to modulate signals, and the modern electromagnetic environment is becoming more and more complex. Therefore, in order to adapt to the development trend of communication diversification and realize the dynamic management, allocation and use of resources, the monitoring and management of electromagnetic spectrum is becoming more and more urgent. One of the tasks of monitoring spectrum resources is to identify signal debugging methods. The correct identification of the signal modulation mode provides powerful help for the subsequent signal analysis, thereby improving...

AI Technical Summary

Problems solved by technology

The identification method of the signal modulation mode is mainly composed of three parts: signal preprocessing, feature extraction and type identification. The traditional method based on the likelihood ratio judgment theory has a large amount of calculation and is difficult to identify. The above-mentioned pattern recognition method based on high-order statistics as features The training complexity is high; the recognition method that simply extracts the in-phase and quadrature components as features and directly inputs them into the convolutional neural network requires a large amount of sample data. The neural network learns noise by itself without denoising means, which makes training more difficult and the generalization ability of the trained model is weak

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