Modulation signal identification method based on wavelet transform and convolutional long short-term memory neural network

Abstract

The invention discloses a modulation signal identification method based on wavelet transform and a convolutional long short-term memory neural network, and the method comprises the steps: firstly obtaining a wireless continuous time signal in advance through a wireless communication system, and forming a data set; secondly, filtering the noisy signal by selecting a reasonable threshold value, andthen reconstructing a wavelet coefficient obtained after processing by utilizing inverse wavelet transform to recover an effective signal; finally, executing the signal feature extraction capability of the convolutional neural network and combining with the memorability of the long short-term memory network, fully learning global features and effectively classifying signal samples with time sequence. A wavelet denoising preprocessing technology is used for suppressing high-frequency noise of an input signal, a convolutional long-term and short-term memory neural network is constructed, globalfeatures are fully learned, and then signal samples with time sequence are more effectively classified; recognition accuracy under a complex environment is improved. therefore, the invention is a modulation identification method suitable for a real channel environment.

Description

technical field [0001] The invention belongs to the technical field of modulation signal recognition, and in particular relates to a modulation signal recognition method based on wavelet transform and convolutional long-short-term memory neural network (CLNN). Background technique [0002] Automatic modulation classification is a very important process before signal demodulation and has various civilian and military applications. With the continuous development of wireless communication technology, the number of modulation schemes and parameters used in wireless communication systems is increasing rapidly. Therefore, the problem of how to accurately identify the modulation mode becomes more challenging. Typical modulation recognition methods include decision-theoretic methods and feature-based methods. Based on the decision theory method, the optimal solution in the Bayesian sense can be obtained by comparing the likelihood ratio with the threshold determined by the Bayesi...

AI Technical Summary

Problems solved by technology

[0003] Purpose of the invention: In view of the problem that the recognition accuracy of traditional modulation recognition methods is significantly reduced when applied to complex comm...

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